Conference Agenda

Climate change and urbanization transform life globally, with direct impacts on each other, yet they are rarely studied together across disciplines. The Synergy Grant urbisphere, funded by the European Research Council (ERC), aims to forecast feedbacks between climate and cities. With new synergies between four disciplines (spatial planning, remote sensing, modelling and ground-based observations), urbisphere incorporates city dynamics and human behaviour into climate forecasts/projections, focusing on within-city dynamics of peoples’ activities and how these can be up-scaled to cities globally. urbisphere is studying inter/intra-city form and function (demographics, mobility, climate adaptation and vulnerability planning typologies), exploring human/socio-economic vulnerability, exposure, risk perception, coping/adaptive measures to climatic stressors and settlement/building typologies.

urbisphere is developing new ways to represent city dynamics for weather/climate models. These models are informed by the urbisphere developed Earth Observation system, using space-borne/airborne sensors and ground based sensors with near real-time data transmission, processing, visualization and central archiving of data from 500+ sensors, including a network of ceilometers, scintillometers, Doppler wind lidars, flux towers combined with street-level and indoor sensors. Combined these measure the 3-dimensional state of the atmosphere and the surface. The sensors are being deployed through an annual cycle, with successful campaigns in Berlin (2021 - 2022) and Paris (2023 - 2024). The sensors are now being deployed in Bristol. These observations are providing both new understanding of urban surface-atmosphere processes and datasets for model evaluation at unprecedented detail. For example, the Berlin campaign helped understanding how an isolated city modifies the atmospheric boundary layer, the relation with human activity cycles and variations above and downwind of the city.

More information on urbisphere is available at: https://urbisphere.eu

The large abundance of materials absorbing short-wave radiation from the Sun and the concentration of people make cities particularly vulnerable to the heat island effect. Although the surface albedo and emissivity of the materials in the urban fabric are key quantitative properties for heat pollution mitigation strategies, these are often lacking citywide. Observing cities from space with high-spatial resolution optical and thermal-infrared sensors can circumvent the in-situ stations’ spatial and temporal coverage limitations.

However, the measurements from space correspond to information relative to a limited number of satellite acquisition geometry and spectral bands, which prevents the accurate computation of needed quantities such as urban albedo maps. In order to extrapolate satellite observations to any upward directions and to the whole spectral domain of interest, a physical model considering the complex 3D structure of urban environments is needed. This contribution describes the SuaBe project, which focuses on designing and implementing a fast, and robust algorithm, that uses the 3D radiative transfer code DART (https://dart.omp.eu/#/) to invert remote sensing images of cities as a 3D distribution of optical properties and temperature, using a geometric urban database. This approach enables us to obtain a 3D model of a city to simulate urban surface albedo and emissivity maps at any date as long as the optical properties of urban elements remain constant. These optical properties can be up-dated with the inversion of recently acquired satellite imagery.

As a case study, we will apply it to Brussels to retrieve surface albedo and emissivity maps at a neighbourhood scale. These results can be considered an asset to be used by urban planners and decision-makers to identify what urban areas should be considered priority candidates for an intervention to mitigate heat pollution, which in turn, shall allow authorities or civil organisations to maximise benefits from limited financial resources. Since the SuaBe’s methodology is based on a robust and rigorous physical model, it can be seamlessly implemented in any other city worldwide, provided that a geometric urban database is available.

Climate change increases stress on urban areas due to the rise in heat waves, which can threaten people’s wellbeing and even lives. Temperature is a crucial parameter in climate monitoring and Earth Observation (EO) systems. Advances in remote sensing technology have expanded opportunities for monitoring surface temperature from space. With numerous satellite thermal missions anticipated in the coming years, there is a pressing need for improved methods to retrieve surface temperatures for cities. While EO satellites are excellent for mapping Land Surface Temperature (LST), the unique properties and geometry of urban surfaces, along with the trade-off between temporal and spatial resolution, pose challenges in retrieving urban surface temperature (UST).

To this end, the EO4UTEMP project explored the use of EO data for monitoring UST from space. EO4UTEMP developed innovative methods and algorithms for producing detailed, accurate, and frequent UST products. A UST retrieval algorithm for high-resolution thermal sensors (e.g., Landsat, ASTER, ECOSTRESS, and the upcoming TRISHNA, LSTM, and SBG) includes emissivity corrections using ancillary information from external sources (e.g. urban surface cover information from Sentinel-2, Landsat) and spectral libraries. The algorithm accounts for the sensor’s viewing angle and considers the fraction of vertical urban facades in the UST retrieval, increasing the accuracy in retrievals. Combined with a thermal imagery downscaling approach, the UST retrieval algorithm allows for the use of low-resolution satellite thermal imagery, therefore increasing the frequency of UST observations. The EO4UTEMP methodology was evaluated using in-situ measured UST from meteorological station measurements in Heraklion, Greece, with a Mean Absolute Error (MAE) of up to 3.6 K for daytime and 1.4 K. The EO4UTEMP methodology is transferable and applicable to cities worldwide and the project showcases new technologies and tools to the EO community and promotes the use of EO data in urban meteorology.

Extreme heat events pose a growing threat to urban populations, with rising temperatures linked to increased mortality. In response, the development of advanced tools becomes imperative for effective mitigation and real-time management of heat-related mortality. This work presents a novel approach leveraging digital twin (DT) concept to estimate mortality associated with extreme heat events, offering both long-term projections and real-time insights for heatwave management. The work is being implemented within the framework of CARMINE project (Climate-Resilient Development Pathways in Metropolitan Regions of Europe).

The DT on heat health risk will serve as a human mortality estimator, employing a Machine Learning model trained on diverse urban indicators to predict heat-related mortality occurrences during summer months and heatwave events. This research establishes digital coupling, facilitating seamless connections between disparate data sources essential for mortality estimation. Key datasets for this utilization include high resolution urban scale modeling coupled with near-real time data incorporating natural (including Nature-based Solutions – NbS) and built environment features, real-time satellite-derived temperatures, weather forecasts, Copernicus C3S, mortality records, socio-economic data and demographics. The latter are among the prime variables to condition population vulnerability and thus the fatality of future heatwaves and to dictate policies to strengthen resilience.

Protocols for accessing datasets and ensuring data security, including user authentication mechanisms, are integral components of the DT.

By integrating advanced modeling techniques with real-time data streams and urban indicators, the proposed DT offers a comprehensive solution for proactive heatwave management. The ability to forecast heat-related mortality enables policymakers and public health authorities to implement targeted interventions, including NbS, asses their performance through the DT, and allocate resources effectively, ultimately enhancing urban resilience to extreme heat events. Furthermore, this initiative will coordinate with the flagship initiative of DestinE to increase its impact and scaling up.

Funded by the European Union (GA 101137851).

For EU policies to be efficiently planned, there is a need for a continental, harmonized, multitemporal and highly detailed indicator on soil sealing that allows the monitoring of the location and the degree of impacts. The European Copernicus Land Monitoring Service has been producing datasets on imperviousness every 3 years since 2006, which are the only high-resolution datasets that enable European wide monitoring. However, after the 2015 reporting year, the input for the production of the imperviousness dataset was switched from mixed inputs to the European Sentinel satellites. While this led to an improvement in the spatial detail from 20 m to 10 m, the change in the input dataset also resulted in a break in the time series as the 2018 update was not comparable to the previous reference years. In addition, the European Copernicus Land Monitoring Service has been producing a new dataset from 2018 onward entitled the CORINE Land Cover (CLC)+ Back Bone which also include a sealed area thematic class. When comparing both datasets with sampled reference data, it appears that the imperviousness dataset substantially underestimates sealed areas at European level. However. The CLC+ dataset only started to be available from 2018 and currently does not include any change layer. To address these issues, we present a harmonized and bias-corrected continental soil sealing combined dataset for Europe for the entire observation period. This new dataset has been validated to be the best current dataset for monitoring imperviousness and soil sealing impacts as a direct input for European policies. Finally, recommendations for future updates and validation of imperviousness degree monitoring geospatial products are given.

Soil sealing – also called imperviousness – is defined as a change in the nature of the soil leading to its impermeability. Soil sealing has several impacts on the environment, especially in urban areas and local climate, influencing heat exchange and soil permeability; soil sealing monitoring is crucial for the Mediterranean coastal areas, where soil degradation combined with drought and fires contributes to desertification.

Some artificial features like buildings, paved roads, paved parking lots, and other artifacts can be considered to have a long duration. In general, these land cover types are referred to as permanent soil sealing because the probability of coming back to natural use is low. Other land cover features included in the definition of soil sealing can be considered reversible. For them, the probability of coming back to natural use is higher. The land cover classes that are included in the reversible soil sealing have been defined with the users of the project, and include solar panels, construction site in early stage, mines and quarries, long-term plastic-covered soil in agricultural areas (e.g., non-paved greenhouses).

The project Mediterranean Soil Sealing, promoted by the European Space Agency (ESA) in the frame of the EO Science for Society – Mediterranean Regional Initiative, aims to provide specific products related to soil sealing and its degree over the Mediterranean coastal areas by exploiting EO data with an innovative methodology capable to optimize and scale-up their use with other non-EO data. The project started in March 2021 and the final products are available in 2024. The project team is led by Planetek Italia, and composed by ISPRA and CLS.

The targeted products are high-resolution maps of the degree of soil sealing over the Mediterranean coastal areas (within 20km from the coast) for the 2018-2022, at yearly temporal resolution with a targeted spatial resolution of 10m.

The involvement of stakeholders and end-users is an essential element of the project. Since from the early stage of the proposal, efforts have been made to reach a diversity of users and stakeholders; the presence of ISPRA in the consortium is a plus for the project in this sense.

Users are grouped into classes: municipalities; sub-national agencies or local governmental institution; national institutions and research centers; regional institutions (EEA) and international (UN).

We would like to present TRIPS, a web application developed as an ESA 5G for l'ART Demonstration Project in collaboration with the Turin Municipality, Municipal Police aimed at integrating advanced ML/DL technologies to address key challenges in urban safety. It has three core modules:

Road Markings Quality Assessment: Using state-of-the-art Computer Vision algorithms on Very High-Resolution (VHR) data, TRIPS provides a diagnostic of road markings quality through a robust pipeline. A domain-adapted neural network is used to extract a visible road mask, which is then used to assist in training the markings model. This process helps the model concentrating on the key elements of the road. Two other helpful features are that the models have been trained under supervision through the laborious process of manually labeling many VHR pictures, and a customized shadow detector model has been created to manage the shadows.

Car Risk Accidents Forecasting: TRIPS incorporates a sophisticated predictive model capable of forecasting car accidents across the city for the next 5 days by analyzing traffic patterns, weathers and street characteristics like intersection type or number of pedestrian crossings. This approach empowers authorities to identify high-risk areas and implement targeted interventions to prevent accidents, ultimately enhancing overall road safety.

Drone-Based Accident Surveillance: During the pilot phase, TRIPS integrates a module that harnesses the capabilities of drones deployed by municipal police for accident surveillance. This module facilitates real-time collection of images and data at accident sites, enabling prompt emergency response and comprehensive post-accident analysis.

In terms of accident prevention and emergency response, TRIPS offers a paradigm change in urban safety management. Through cooperation between the Police, the Turin Municipality, and ESA, TRIPS is an example of innovation using EO data and ML technology to address smart mobility in urban areas.

The African Development Bank (2014) reports that 53% of African roads are unpaved, with these roads being vital for the continent's economic and social development but requiring efficient maintenance strategies to remain motorable. This study, supported by the European Space Agency, responds to the challenge of irregular and inaccurate traditional condition surveys by introducing an innovative, cost-effective machine learning (ML) solution aimed at aiding local road authorities to monitor and plan road maintenance more effectively. This research builds on earlier initiatives by TRL, which demonstrated the efficacy of classical ML models in analysing Tanzania's unpaved roads through high-resolution satellite imagery. Despite the potential economic advantages of the proposed ML methods over traditional techniques, stakeholders considered high resolution imagery to be expensive. This trial involves leveraging both medium and low-resolution satellite images to assess road conditions in Madagascar and Malawi to make significant savings on imagery costs. Our approach involves Multimodal ML using classical models trained on values derived from image statistics. Our findings indicate that Multimodal ML achieves commendable accuracy (87%) with high-resolution imagery, which declines by 8% and 7% for medium and low resolutions, respectively. This study underscores the potential of ML technologies to significantly enhance the assessment and maintenance of unpaved roads through optical satellite imagery analysis, presenting a promising path for cost-effective road management strategies.

The PRIMARY project aims to enhance air quality monitoring, especially in urban areas, using data from the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission. By analyzing PRISMA's hyperspectral data, the project seeks to understand atmospheric aerosols content and composition, crucial for assessing environmental and health impacts, particularly in cities. Spatial resolution of PRISMA data (30 m) and artificial intelligence play a key role in overcoming challenges such as spatial resolution limitations and the complexity of the inverse problem in satellite-based atmospheric studies.
To train neural networks for estimating aerosol characteristics using PRISMA data, a synthetic PRISMA-like dataset was generated with support from the Copernicus Atmosphere Monitoring service (CAMS). The training dataset generation exploited the libRadtran radiative transfer model. Trained AI modules are then applied to PRISMA images to produce atmospheric aerosol products.

Field campaigns were conducted in Rome (autumn 2022) and Milan (winter to summer 2023) to validate the PRIMARY project's outcomes. Moreover, drone measurements are being integrated to support validation activities. Preliminary results are encouraging and seems aligned with ground based measurements.
Data fusion products combining PRIMARY results with air quality data from missions such as Sentinel-5P, Sentinel-3 and GCOM-C are also under consideration.

In many cities the population is exposed to elevated levels of air pollution. Often, the spatial distribution of local air quality throughout urban areas is not well known due to the sparseness of official monitoring networks, or due to the inherent limitations of urban air quality models. Satellite observations and emerging low-cost sensor technology have the potential to provide complementary information. An integrated interpretation, however, is not straightforward. The CitySatAir project (part of ESA’s EO Science for Society program) investigates how satellite data of atmospheric composition can be better exploited for monitoring and mapping urban air quality at scales relevant for human exposure.

Focusing particularly on the nitrogen dioxide product provided by the TROPOMI instrument on the Sentinel-5P platform, we investigate different approaches for combining this data with other information such as from models and air quality monitoring stations. We choose four contrasting study sites across Europe (Madrid, Oslo, Rotterdam, Warsaw) differing in size, pollution levels, dominant emission sources, and cloud cover.

For Oslo and Warsaw, we use the Sentinel-5P NO₂ data in conjunction with the urban dispersion model EPISODE to bias-correct the underlying bottom-up emission dataset. The results indicate that, when the model is run with the satellite-corrected emission dataset and validated against air quality monitoring stations, the model error (RMSE) decreases for all stations by up to 20%. The updated model dataset is then used to assimilate observations from monitoring stations and low-cost sensors. In addition, we exploit the synergy of TROPOMI and EPISODE data by deriving surface NO₂ data and carrying out geostatistical downscaling to provide a satellite-based surface NO₂ dataset at scales relevant for human exposure.

For Madrid, Rotterdam, and Warsaw we developed a versatile urban dispersion model able to calculate both surface concentrations of NO₂ at street level and NO₂ column concentrations matching the TROPOMI observations. Urban emissions are described by proxies taken from open data, where emission factors are updated periodically to best match the observations from either ground or space. Compared to the CAMS regional ensemble, local biases are reduced considerably, especially if in-situ measurements are also assimilated in the simulated concentration fields using optimal interpolation.

The multi-annual reanalysis of hourly urban air pollution concentrations at street level provides a very rich data set, which demands special user-friendly tools for exploration and analysis. The data sets for the different cities are showcased in the Lobelia Explore viewer. Lobelia Explore is based on a serverless architecture: as a result of the user interacting with the viewer, the web application requests air pollution data from the cloud as static files and uses this data to render maps, display charts and aggregate data over user-defined areas, all of this browser-side. This architecture eliminates the need of on-demand data processing and reduces maintenance costs.

This work describes preliminary attempts aimed at creating a dataset of daily averages of aerosol optical depth (AOD) on an intraurban scale (300m) using MODIS MAIAC AOD, SEN3 SYN, and AOD from ERA5 reanalysis models. Our preliminary efforts were aimed at understanding the quality of available AOD products by comparing them with daily average measurements provided by the AERONET network for the Italian peninsula during the reference period 2019-2023. MODIS MAIAC AOD proves to be state-of-the-art in satellite AOD reconstruction, while SEN3 SYN correlates less and shows a significant bias when compared with AERONET.

Our efforts are oriented in two directions: a) evaluating whether SEN3 SYN is mature enough to deliver unbiased AOD products on an intraurban scale on a daily, weekly, and monthly basis, possibly using other sources of information such as DEM, latlon, and LST; b) performing data imputation of missing observations using AOD from reanalysis models such as ERA5.

Regarding AOD correction on an urban/intraurban scale, we are evaluating pixel-based approaches such as linear/nonlinear/GAM regression algorithms fueled by the combined use of SEN3 SYN, MODIS MAIAC, ERA5 AOD, and auxiliary data such as MODIS land surface temperature and climatic data.

Our findings demonstrate that there is room for further improvement of AOD products by imputing missing AOD values and by further calibrating AOD using regression models fed with available AOD estimates and auxiliary data.

This work is part of a collaborative project funded by ASI and called APEMAIA (Assessment of PM Exposure at the intra-urban scale in preparation for the MAIA mission). The project is designed to investigate the potential of MAIA by developing a multi-modular system for extracting PM concentrations at the intra-urban scale using Artificial Intelligence techniques.

The environMENTAL EU project aims to investigate the impact of major global challenges on mental health and brain health across the lifespan, including climate change, urbanization, and psychosocial stress. The project also seeks to develop prevention techniques and early interventions in this context.

Earth observation data is used to provide a comprehensive set of spatial information layers that may influence mental health and behavior. Our environmental datasets focus on urbanicity, greenness, water bodies, and elevation information, which will be adjusted to the geographic regions of the studied cohorts and linked to geographical positions and their corresponding data. We examine the relationship between environmental factors and mental health, utilizing global datasets such as the TanDEM-X Digital Elevation Model (DEM), the World Settlement Footprint (WSF) 3D data, night time lights data, local sun incidence angel corrected sun energy data, multi-spectral data, atmospheric data (NO2, SO2, CO, O3, and CO2 concentrations), cloud cover, air temperature, precipitation, and air pollution data.

This work presents first results from combined analysis of UK Biobank cohort data and spatial urban neighborhood metrics on mental well-being indicators such as the neuroticsm score. The neighborhood metrics are calculated for suitable geodata within different diameters ranging from 300 m to 7500 m to include spatial context information in point analysis. From geodata, the corresponding values are extracted at the patient coordinates of the cohorts and a cohort population density normalized analysis of defined bins above UK Biobank cohort data value thresholds is performed. First results indicate correlation of green indices, night time light data and building volume metrics with mental health scores within the UK Biobank cohort data. We hypothesized that environmental factors could also serve as proxies for the social environment and significantly influence mental health, particularly when coupled with the presence of urban green and blue spaces.

Overall, our research provides first insights into the link between geo-environmental factors and mental health outcomes, providing valuable information for policymakers, urban planners, and public health professionals aiming to create healthier and more sustainable living environments.

Urban areas, home to the majority of the global population, significantly contribute to environmental degradation due to substantial energy consumption and greenhouse gas emissions. In the European Union, buildings account for 40% of energy use and 36% of CO2 emissions. With 35% of buildings over 50 years old and 75% considered energy-inefficient, urban retrofitting is crucial for enhancing sustainability. Compounding these challenges, current Energy Performance Certificate (EPC) databases suffer from data gaps, lack of standardization, and restricted access, hindering effective action towards the European Green Deal’s goals for a low-carbon future and net-zero emissions by 2050.

In this framework, the ESA BEE.AI (Building Energy-efficiency Estimation with Artificial Intelligence) project introduces an innovative approach to improve urban building energy performance assessments using advanced deep learning and Earth Observation (EO) data. In particular, the intended solution integrates crowd-sourced street-level optical and thermal imagery, satellite-based very high-resolution top-view visible and near-infrared imagery, high-resolution land surface temperature metrics, and urban morphology metrics. Together with existing EPCs, BEE.AI aims at generating detailed maps categorizing building energy efficiency from "A" (most efficient) to "G" (least efficient), pinpointing retrofitting opportunities. Here, besides a baseline solution implementing an end-to-end deep-learning architecture, BEE.AI also envisages an advanced system which additionally extracts and integrates features explicitly providing insights into building characteristics (i.e., building age, construction material, presence of PV panels) and thermal properties.

Pilot studies across Denmark, Austria, and Italy, engaging stakeholders from the business sector such as real estate developers, local governments, and energy companies, will showcase BEE.AI’s capabilities. These collaborations are vital for tailoring the solutions to regional architectural styles and climate conditions and ultimately setting a new benchmark for sustainable building practices, supporting a transition to a low-carbon future.

The European Green Deal sets ambitious targets emphasizing the urgent need for the ecological transition in Europe, with a specific focus on enhancing renewable energy usage. A pivotal aspect of this initiative is increasing the renewable energy share to 32% by 2030, facilitated by amending the Renewable Energy Directive to promote decentralized sources like rooftop solar installations. Numerous European cities and municipalities are advancing this goal by incentivizing solar power and streamlining approval processes.

In this framework, the DLR Solar-DE project plays a crucial role by providing detailed spatial data essential for urban planners and policymakers to scale up rooftop solar resources effectively. This project has conducted a comprehensive mapping of solar installations across the 20 million buildings in Germany using high-resolution digital orthophotos (20cm spatial resolution), surface and terrain models (1m spatial resolution), and building perimeter vector data from the Federal Agency for Cartography and Geodesy, integrated through advanced machine learning and artificial intelligence (AI) techniques. Additionally, a specialized model assesses potential roof installation sites for photovoltaic (PV) panels by calculating the possible electrical output based on peak sunshine hours, roof inclination, and orientation as well as shading effects from neighboring trees or buildings. This initiative marks the first comprehensive national survey of Germany's solar capacity and identifies significant expansion opportunities, thereby providing essential data at both the building and administrative levels to support localized and national energy policies.

The insights from Solar-DE are instrumental in facilitating targeted investments and strategies for renewable energy development, thereby contributing to the broader goals of energy transition and greenhouse gas reduction. This project not only enhances our understanding of current solar energy infrastructures but also aids in planning future expansions to meet environmental targets.

Urban decarbonisation is one of the most urgent challenges in the fight against climate change, and space technologies are emerging as critical tools to accelerate this transition. With their invaluable contributions, it is possible to better understand and manage the urban environment and promote the adoption of sustainable energy strategies.

Recent developments in digitalisation and green technologies are revolutionising the way cities can reduce emissions and optimise resource use. For example, the integration of satellite data with space-based monitoring systems allows real-time mapping of urban energy efficiency, identifying issues such as heat islands and storm runoff times. This data is essential for designing targeted interventions to reduce emissions and improve the integration of renewable energy sources.

In addition, digitalisation and the use of artificial intelligence in cities are emerging as key trends in achieving sustainability goals. Combining these technologies with advanced geospatial data not only improves energy efficiency, but also strengthens urban resilience and promotes smarter and more sustainable resource management.

In this context, the Decarbonisation Twin Support (DTS) system integrates Earth Observation (EO) data with geospatial and environmental data to create dynamic digital twins of urban environments. These digital twins are designed to monitor and optimise carbon emissions and the efficiency of renewable energy integration in urban environments. By simulating real-world conditions and analysing historical and real-time data, the DTS system provides valuable insights that can guide urban planners, architects and policy makers in reducing carbon footprints.

This approach promises to transform cities into centres of sustainability, where energy is optimally managed and emissions are significantly reduced.

Access to energy is identified as one of the basic needs towards a decent life with better chances (SDG 7). Whereas in Europe, main initiatives concentrate on implementing the European Green Deal and related national policies, in other parts of the world providing access to energy is a still ongoing task and often handled in a more pragmatic way.

The need of access to consistent and reliable energy as guarantee for economic development remains a challenge in many fast growing and rapidly densifying urban agglomerations (Africa, SE-Asia). Cities are less managed when it comes to a well-designed energy infrastructure, understanding the dissemination grid is crucial. Private initiatives and investors provide access to electricity, thus the urban fringe is often intermingled with off-grid energy units, such as mini-grids, preferably run by diesel generators. Few houses run Photovoltaic (PV) on their rooftops. Air quality is critical, space is limited and open suburban regions convert to densely populated places within few years. This is the common setting where International Financial Institutions (IFIs) engage in supporting the transition towards sustainable solutions in combination with fulfilling SDG 7, serving the ranging needs within urban agglomerations and in the rural areas.

EO can act as overarching element by providing a better understanding of the urban dynamics, and thus in tailoring the financial support accordingly. Focus is drawn to find best fitting, affordable and sustainable solutions, may this be solar rooftop solutions, hydropower, wind energy, or even biogas. This first stage does not necessarily consider commercial VHR1 satellite image data.

Within the Global Development Assistance Project on Clean Energy (GDA-CE), multi-scale approaches are sketched linked to sites if ranging extent. For Armenia, as one example, a coarse solar potential analysis was prepared on national level, benefiting from HR Sentinel-2 imagery (timeline) and most recent terrain data, going beyond the commonly used global solar atlas. A more detailed solar rooftop analysis is performed considering VHR stereo data for the capital Yerevan, emphasising on common challenges when working with spaceborne data. The latter proofs sufficient for the first stage of dimensioning potential investments. When characterising the urban structures regarding their suitability for roof-top installations itself, generic understanding of building orientation, types & sizes, distribution, and specific roof-top characteristics (obstacles, age, sub-rooftop level) is of interest. More detailed aerial flight planning is considered as far too costly and gets rather replaced by local drone flights once investment planning reaches engineering level (static).

When working with IFIs such as the World Bank, besides providing the technical solution itself, the combination of information layers of higher and lower granularity is considered valuable, as long as transferability is given. input parameters are often not optimal, project timelines are prearranged and thus creative and pragmatic solutions adapting to national specifics are necessary. This presentation provides a wrap-up on how multiple scales are reasonable within different planning stages and perspectives, showcased in multiple locations (cities and rural areas). It supports the engagements of the IFIs, being aware of limitations of regional scale vs. VHR data analyses. Benefits of receiving a most recent situational picture, linked to timeline and understanding the contextual options often surpasses this and build the base for a detailed trade-off analysis.

Smart Cities prioritise environmental impact reduction and the green economy to create and maintain healthier, more sustainable places to live and work. Almost three quarters of European citizens now live in cities and this figure is expected to reach 80% by 2050. When it comes to energy, cities are focused on a secure and sustainable supply of clean energy, as the risks of climate change and the need to reduce our carbon footprint grow ever more real. Becoming energy efficient, it means also engaging technologies to reduce emissions in the transport sector and improve infrastructure to support a green transition. The presentation will provide an overview of operational solutions -environmentally and economically sustainable-which have been developed for the needs of the cities.

As urban centres grow, the global energy landscape is undergoing a dramatic shift toward decentralization, digitization, and decarbonization to meet the 2015 Paris Agreement's stringent goals of capping the rise in global temperatures to below 2°C. This shift is even more critical in cities, where energy demand is higher.

In this ongoing urban-centric transformation, wind energy is emerging as a key player due to its efficiency and the falling costs driven by technological advances. In particular, the installation of wind turbines (WT) is proving vital for creating decentralized power systems within and around cities. This approach not only helps mitigate transmission congestion but also bolsters energy security by generating power closer to its consumption points.

However, the variable nature of wind power poses significant challenges in urban settings, where energy supply consistency is crucial. The fluctuating output of turbines can lead to periods of both surplus and insufficient energy, highlighting the need for accurate and sophisticated energy modelling. This modelling is critical to optimize urban energy grids and requires up-to-date and precise data on infrastructure locations, which is frequently lacking.

To bridge this gap, the ESA EO4Energy project employs advanced deep neural networks and leverages Sentinel-1/2 satellite imagery to map onshore WTs and identify active coal power plants (CPP). Specifically, targeting CPPs is crucial not only because they are major emitters of CO2 but also because they offer potential as heat storage solutions that could stabilize the urban grid during low wind periods by storing excess energy.

The initial results from 100 test sites are extremely promising, demonstrating the project’s effectiveness in accurately identifying WTs and CPPs. This success paves the way for applying these technologies globally to improve urban energy planning and infrastructure, ensuring cities are more sustainable and better prepared to meet future energy demands.

The Earth Observations Toolkit for Sustainable Cities and Human Settlements enables the use of Earth observations to advance Sustainable Development Goal 11 and the New Urban Agenda. The Toolkit (https://eotoolkit.unhabitat.org/) represents an ongoing effort to put Earth observations (EO) data and tools into context for analysts in national and city governments, and at local community level. Additional target audiences include policy and decision makers, executive managers and the urban sustainability-interested public. The Toolkit focuses on end user stories that highlight EO applications to improve the timeliness and quality of urban-related indicators, guide policies, and support sustainable urban development. It is a multi-stakeholder partnership that facilitates engagement among local communities, cities, national agencies, and EO experts. It also aims to promote knowledge sharing and collaboration between local communities, cities and countries.

RethinkAction (https://rethinkaction.eu/#project) is an EU Horizon-funded project running from 2021-2025. The project aims to develop an innovative digital platform to improve land use planning, enhance climate resilience, and engage local communities in sustainable solutions and behavioral change. The project has a strong emphasis on multi-stakeholder co-creation across the six case study regions for the project located in Italy, France, Hungary, Spain, Portugal, and Sweden.

The platform includes a database of potential Land Use Adaptation and Mitigation Solutions (LAMS)and land-based policy recommendations that cities can access based on the challenges that they face and factors from their local context. It also includes a multiscale evaluation framework for future scenarios consistent across local, EU, and global scales and dynamic models for the integrated assessment of potential solutions.

At this phase of the project, a prototype of the platform has been developed and will be presented to stakeholders for their feedback via an upcoming workshop series.

The demonstration will illustrate how to navigate the GHSL website and to access the different data sets, tools and knowledge products.

Urban TEP (Urban Thematic Exploitation Platform) https://urban-tep.eu serves as an information hub providing data and services to support sustainable urban development. Launched as an ESA project in 2015, it was conducted by a consortium led by DLR (with Terradue, GISAT, Brockmann Consult, and IT4I), in cooperation with a team of public and private experts from the Earth Observation sector. The fly-through presentation will outline the resources and services available on the Urban TEP platform, with a focus on the latest cooperation with UN-Habitat. This collaboration leverages the platform’s technologies and integrates a new generation of global urban datasets (e.g. GHSL and World Settlement Footprint suites) to streamline the production of high-quality urban information for SDG indicator 11.3.1 worldwide, in an accessible and interactive manner.

This study aims at investigating the suitability of very-high resolution satellite imagery, e.g., Pleiades, WorldView-2 for classifying the dominant urban tree species in both public and private areas and estimating the tree height. Tree species differentiation is challenging in cities, as trees can be lined up or grouped in patch, with a wide range of plant species, high spectral similarity of vegetation types, and due to the complexity of the urban environment (buildings, shadows, open courtyards).

In the present study, we have implemented the so-called “object-based classification of urban tree species from very high-resolution satellite imagery” methodology we published in Sicard et al. (2023), that is an object-based classification using Random Forest classifier with different textural features extracted from tree canopy and grassland (lawn/turf) to identify and map dominant types of vegetation. Four spectral bands (blue, green, yellow, red) and four texture features (i.e., energy, entropy, inverse difference moment, Haralick correlation) were the most efficient attributes. Tree height estimation was performed using Pleaides tri-stereoscopic imageries and the photogrammetric tool MicMac of the French National Geographic Institut and the ForestTools package.

We applied the methodologies in two cities Aix-en-Provence (France, 50km²) and Florence (Italy, 80km²) where about 420,000 and 555,000 canopies were successfully classified in 22 and 20 dominant species with an overall accuracy of 84% and 83%, respectively. We found that about 85% of trees in both cities are in private lands. The highest classification accuracy was obtained for Pinus spp. and Platanus acerifolia in Aix-en-Provence, and for Celtis australis and Cupressus sempervirens in Florence. For the tree height, we obtained an absolute accuracy of 1.8m.

As cities expand, the pressure on natural ecosystems intensifies. Urban and peri-urban areas try to overcome with conflicting demands, which results in fragmented green infrastructure, and severed links between rural and urban zones. The European Union Biodiversity Strategy for 2030 recognizes this urgency and calls for action. The ambitious goal of "Greening urban and peri-urban areas" aims to reverse these trends. Similarly, the UN Sustainable Development Goals indicate the need for inclusive and accessible green urban areas (SDG 11.3).

The concept of Green and Blue Infrastructure (GBI) represents a strategic network of natural and semi-natural areas, thoughtfully integrated into urban and peri-urban landscapes. GBI weaves together green spaces, water bodies, and wildlife corridors to create a dynamic fabric that sustains life and mitigates environmental challenges.

By combining the implementation of GBI together with prioritising accessible green spaces, cities and regions can combat the dual crises of biodiversity loss and climate change. To support policymakers in the identification of these priority areas, a methodology was developed by the authors to map urban green infrastructure in the European Functional Urban Areas. This methodology used several High Resolution Layers (HRLs) and the Urban Atlas from the Copernicus Land Monitoring Service (CLMS) as well as complementary data from the Open Street Map (OSM), improving the thematic and spatial resolution provided by Urban Atlas. As a result, two datasets were produced corresponding to 2012 and 2018 which allows monitoring changes between the defined 8 GBI classes. It also allows quantifying the amount of GBI created within this period. Results are presented in transparent and easy to understand dashboards that allows users to analyse and download GBI data in Europe. The work is carried out in collaboration of the European Environment Agency and DG REGIO of the European Commission.

Compound dry and hot conditions have the potential to result in adverse socio-economic and ecological impacts in urban areas; they are usually associated with larger repercussions and risks than individual extremes. The thermal properties of vegetated urban parks enable them to function as climate modifiers, thereby increasing the adaptive capacity of cities. In this work, a climatology of compound drought and heat wave events (CDHW) is firstly derived for selected megacities worldwide (cities with population over 10 million residents). To achieve this, reanalysis data (ERA5-Land) and extreme indices (Excess Heat Factor for heatwaves and the Standardized Precipitation Evaporation Index for droughts) are used. Thermal patterns inside cities during CDHW events are derived using remotely-sensed Land Surface Temperatures (LST) (from Landsat 8/9 and MODIS Aqua/Terra satellites) and the co-occurrence between air temperature and LST anomalies is assessed. Then, the thermal regime of large-scale parks within megacities is evaluated under both typical conditions and during the identified CDHW extremes. Remotely sensed data underscore the role of urban green in mitigating the increasing heat and drought hazards confronting cities, driven by local and global climate change.

Urban green spaces are an important component in enhancing urban livability. Therefore, there is a need for sustainable management of these spaces, which can be acquired by using Earth Observation (EO) technology, to maintain an ecological balance, while mitigating the impact of urban heat islands. By harnessing high-resolution EO data, the S4UG project aimed to provide city managers and planners with advanced tools for informed decision-making in urban growth modeling and ecological assessments, particularly focusing on green and blue infrastructures, urban planning, and urban ecology. In this sense, stakeholders from twelve European cities were involved in defining the needs that arise in green infrastructure mapping in terms of resolution, frequency, and accuracy of the data. While Sentinel-2 imagery is suitable for monitoring urban spaces, due to covering vast areas frequently, their spatial resolution is not always enough to detect smaller green areas, specific to urban environments.

To overcome the limitations of Sentinel-2's image resolution, our research conducted during the S4UG project, focused on using super-resolution (SR) techniques for detailed vegetation extent mapping in urban environments. This application involved using images from Sentinel-2 satellites, which have a resolution of 10 meters, and from SPOT 7 satellites with a 1.5-meter resolution. The SR techniques were used to create synthetic imagery with a finer resolution of 2.5 meters. This improved resolution offered more accurate mapping and monitoring capabilities, highlighting its usefulness through services that provided detailed up-to-date land use/land cover maps and vegetation health monitoring, correlation between vegetation health with environmental factors over time and analyzes of green spaces accessibility for inhabitants. These capabilities proved valuable for stakeholders involved in the project, who require detailed spatial data to make informed decisions, showing that the exploitation of SR techniques at application level has the potential to enhance the added value of the provided solutions.

Urban planning is traditionally centred on human perspective, overlooking the needs of animals and plants that in turn help fostering liveability in cities. To address this bias, the concept of green urbanism emerges as a key driver for sustainable urban development. Nevertheless, the description of these habitats is usually not holistic, lacking the common accent to describe inter-specie interaction.

In response, we propose a novel methodology for characterising urban habitats showcased in an application to three distinct urban case studies: the Functional Urban Areas (FUA) of Vienna (Austria), Munich (Germany), and Genoa (Italy). Our approach transcends anthropocentrism by adopting an agnostic perspective—one that embraces the diverse living entities within co-habitative landscapes.

The classification is composed of four 10-meter rasters for each city, each with a separate set of variables focusing on different aspects of urban metabolisms, such as urban morphology at local and landscape scale, anthropic imprint, and biophysical conditions. The workflow scrapes from open data with global validity, resampling at a 10-meter resolution with a 100-meter moving window algorithm. After preprocessing, the pipeline ends with a combination of MiniBatch and Elkan KMeans algorithms.

The resulting rasters bring insights into distinct urban habitat classes and can provide meaningful observations alone or combined for both urban planners and natural scientists. The classification goal is to integrate with models for optimization and multi-criteria decision making. The role is not being an omniscient knowledge base, but a multi-disciplinary common understanding of urban habitats. The tool enables two methodologies: selecting a site for architectural design or ecological research, given a threshold of targets for liveability and conservation, and comparing different sites from different regions. The tool is developed with scalability in mind, and the next step will be to expand to all European FUA.

Cities are facing too many challenges. Urban vegetation, in particular trees, are essential as they provide services in terms of air pollution mitigation, freshness, biodiversity, and citizens’ well-being. The main aim of the European project AIRFRESH “Air pollution removal by urban forests for a better human well-being” is to quantify the environmental benefits provided by urban trees at city-scale in two front-runner cities: Florence (Italy) and Aix-en-Provence (France). To avoid a large underestimation of the quantification of benefits, a consistent inventory of vegetation within private and public areas is needed. Based on an object-based classification from very-high resolution satellite imagery (WorldView-2), we have detected and classified about 550,000 and 414,000 trees, and grassland, in the two study areas. Then, we applied the AIRTREE multi-layer canopy model and WRF-Chem model for the year 2019. We have also considered species-specific parameters, such as tree morphology (height, diameter at breast height, and crown leaf area), leaf habit (deciduous/evergreen) and eco-physiological responses to environmental factors. Finally, we have mapped the annual removal capacity of each urban tree for the most harmful air pollutants in cities, i.e., tropospheric ozone (O₃), nitrogen dioxide (NO₂), and particulate matter (PM_2.5, PM₁₀) in addition to carbon dioxide (CO₂). Deciduous broadleaves have high capacity to uptake O₃ and NO₂ (Platanus x acerifolia: 1.25 and 0.40 g m^-2 per year). Compared to deciduous broadleaves, evergreen conifers, such as Cupressus spp., showed higher performance in PM removal (11.5 and 2.5 g m^-2 per year for PM₁₀ and PM_2.5). We identified tree species with the highest CO₂ uptake capacity with values up to 2.5 kg m^-2 per year for Cedrus atlantica.

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Smart and Green Cities Task Force initiative brings together key players in the smart cities’ ecosystem to foster the development of sustainable solutions leveraging the use of space applications. The demo will showcase some examples use cases which generate green, societal, and economic impact in the cities by means of space data and assets.

The Urban Atlas suite of products gives users access to detailed land cover/land use maps for 788 Functional Urban Areas across Europe. The demo will showcase some of these FUAs and will introduce some basic concepts about the product.

GTIF is an ESA EOP initiativ to showcase the value of EO for addressing information needs in the context of the green transition and to develo innovative capabilities that provide decision support to various users and stakeholders. We will briefly provide an overview of the GTIF initiative and showcase some selected GTIF capabilities in the context of the sustainable cities and renewable energy domains as part of a live demonstration.

While urban populations grow, cities are ultimately confined in space, needing to accommodate diverse social, ecological, and economic functions. Cities worldwide face the challenge of creating integrated urban environments that balance growth ambitions with new standards for green growth, promoting biodiversity, mitigating climate change, and supporting inclusiveness and quality of life.

Urban Nature-Based Solutions (NBS) offer a multifaceted approach to addressing complex urbanization challenges. As cities grapple with limited space amidst burgeoning populations, NBS emerge as indispensable tools for fostering sustainable development. Monitoring and evaluating the impact and potential of NBS activities are inherently challenging due to the complexity of urban environments and the dynamic nature of these solutions. Herein lies the value of EO technology, offering a bird's-eye view of urban landscapes and facilitating continuous monitoring at various scales. EO enables the systematic collection of high-resolution spatial data, providing insights into vegetation dynamics, land use changes, and environmental conditions over time. EO enables near real-time responsiveness to environmental shifts and evaluation of NBS effectiveness, enhancing the resilience of NBS interventions in the face of urban challenges such as climate change and population growth.

Based on the results of a UNEP funded urban NBS activity, we will illustrate how EO enables near real-time responsiveness to environmental shifts and evaluation of NBS effectiveness, hence enhancing the resilience of NBS interventions in the face of urban challenges such as climate change and population growth. We will shed light on the technology and provide practical use cases from around the world for the applied use of EO to underpin urban green management and planning, emphasizing how modern EO technology can be used to create and maintain an accurate and updated urban information.

World from Space is developing UpGreen, a service that accurately assesses, predicts, and proposes urban green infrastructure using advanced Earth Observation and geospatial methods. A comprehensive understanding of the dynamics of urban greenery is ensured by using multi-sensor, multi-resolution, and multi-temporal approaches. The service comprises three subsequently interdependent modules: UpGreen Assessment, UpGreen Prediction, and UpGreen Vision.

UpGreen Assessment utilizes multispectral data and beyond, to provide detailed delineation, segmentation, and allocation of attributes and functions of urban green spaces. The information gathered per green segment shall include for instance: greenery type, vitality, density, height, biomass, cooling effect, connectivity, accessibility, air cooling efficiency, air pollution blocking, shaded area cast and others.

UpGreen Prediction utilizes advanced AI algorithms and vast amounts of EO and other data to forecast future scenarios for urban green infrastructure with confidence.

UpGreen Vision provides actionable insights for optimal urban green planning based on the city's preferred ecosystem services targets. The recommendations include suggesting the most effective greenery placement distribution, types and quantities to maximize environmental and socio-economic benefits.

The service is a technical response to domain requirements gathered in the preceding ESA Feasibility Study. In summary, those are (1) holistic understanding and strategic planning of urban green, (2) trend analysis and forecasting urban green health, (3) data interoperability for better stakeholder engagement. UpGreen demonstration pilot is currently being developed within an ESA project: Development and Verification of Urban Analytics (4000143727/24/I-DT). The business model with go-to-market activities and first partnerships are already set up and fully operational commercial product-as-a-service is scheduled to be completed after the end of the project. A consortium partner ASITIS will be UpGreen's product manager.

UpGreen will assist cities in making informed decisions towards sustainable urban development by enhancing ecosystem services, urban resilience, and citizen well-being through efficient nature-based solutions.

Satellite image processing engineers, biodiversity experts, and artificial light experts in Cerema (Center for Studies and Expertise on Risks, Environment, Mobility, and Urban Planning) worked together to better quantify the pressure put by Artificial Light At Night (ALAN) on biodiversity. The purpose is to identify priority areas to work on public and private lighting. Indeed, most animals are active at night and sensitive to ALAN. Preserving and restoring an ecological network supportive to nocturnal wildlife is imperative.

Satellite imagery shows a substantial potential to map ALAN as it covers large territories several times per year, at different spatial resolution (global, national, regional scale, or even individual lighting sources thanks to very high resolution).

LuoJia 1-01 is a Chinese experimental satellite that has taken night-time images over France in 2018. Its 130 m spatial resolution enables to study ALAN at a neighbourhood level. These freely available radiance data have a low level of processing and must be orthorectified before use. Clouds also need to be detected and masked.

Cerema processed these 217 images over France and combined them to build a nearly national map of ALAN, produced at the departmental level. Some departments are missing because of the lack of acquisitions, or the dense cloud cover. Orthorectification was achieved with Ground Control Points (GCP) from the Copernicus High Resolution Layer (HRL) Impervious Built-Up (IBU), as ALAN and imperviousness were shown to be strongly correlated.

The nearly national map of France ALAN produced will be freely available for all public entities. Such a map can be produced over other European countries, depending on the available LuoJia 1-01 data.

This map is interesting for biodiversity and lighting experts. Facing these data with other sources (local taxes database or biodiversity maps) can provide even more valuable information.

Green Urban Sat is a two-year project (2022-2024) labelled and co-financed by the Space for Climate Observatory (SCO) and conduced by Cerema (Center for Studies and Expertise on Risks, Environment, Mobility, and Urban Planning) in collaboration with LIVE/A2S laboratory, TerraNIS company, and the city of Nancy. In this project, methods and tools were developed in order to generate a geospatial database describing precisely the urban vegetation cover, which will help to better evaluate some ecosystem services. The urban environment is highly disparate and used to be set aside due to spatial resolution issues. Urban vegetation maps are usually very basic, and only made of two classes : high and low vegetation. With the arrival of Very High Resolution (VHR) satellites such as Pléiades, it is now possible to infer urban issues with stronger accuracy. Green Urban Sat framework discriminates three vertical layers of vegetation, then detects several vegetation formations (isolated tree, wood, narrow band of trees, etc.).

The project is based on stereoscopic Pléiades imagery, acquired at different periods of the year, enabling to derive several indicators describing the vegetation (height, surface, orientation, vegetation type, landscape type, NDVI, etc.). This set of quantitative attributes and indicators will allow to feed a group of decision aid tools for municipalities. A demonstrator is being produced over the city of Nancy (France), and the method is duplicable over other cities.

Urban growth prediction is essential for sustainable urban planning, requiring accurate and reliable models. Satellite-based Earth observation (EO) time series data offer valuable insights into past and allow to conclude on future trends of urban development. However, existing models often struggle to incorporate detailed local information, leading to inaccuracies in growth predictions, or require plenty additional datasets. To address this challenge, we utilize the Spatio-Temporal Matrix (STM) approach, which leverages EO data to generate spatial and temporal characteristics of neighborhoods.

In this study, we applied the STM-based model coupled with a multi-layer perceptron (MLP) for settlement growth prediction in Huê (Vietnam), Surat (India), Ho-Chi-Minh City (Vietnam), and Abidjan (Ivory Coast). Our research aims to assess the model's ability to predict settlement growth while avoiding restricted or intra-urban areas without incorporating additional datasets besides multitemporal settlement layers (World Settlement Footprint – WSF).

Using the WSF-evolution dataset, we achieved promising results, indicating the STM-based model's effectiveness in settlement growth prediction. Compared to baseline results of the SLEUTH model, our approach produced more realistic growth patterns, minimizing predictions in restricted areas without the need for additional layers.

This study highlights the potential of the STM-based model as a reliable tool for urban growth prediction based on EO information products, offering enhanced accuracy and independence from external datasets. By providing insights into future urban development while respecting local constraints, our approach contributes to more sustainable urban planning practices.

Urban areas necessitate effective management strategies to mitigate natural risks and protect communities. By harnessing Sentinel-1 radar images, the European Ground Motion Service (EGMS), an integral component of the Copernicus program, provides comprehensive insights into ongoing ground deformation processes with high spatial resolution and precision of the measurements.

The EGMS is produced over the Copernicus participating countries and is updated annually. The most recent data release covers the period January 2018 – December 2022. The product is made available to users under an open data policy through a dedicated data visualization, interaction and download platform: the EGMS Explorer. Every year, the EGMS produces a massive amount of ground motion data, equal to 13 billion measurement points, each containing a value of the ground motion velocity, a time series describing its evolution over time, and a series of quality parameters.

The EGMS is a deferred-time, multi-purpose mapping, and – to a certain extent – monitoring tool for active ground motion in urban areas. The density of measurement points is the highest in the urban environment, where the so-called permanent scatterers are abundant (buildings, bridges, and man-made structures in general). The EGMS shall be intended as a baseline product, which shall be complemented by in situ or other remotely sensed measurements to achieve the high level of precision of measurements that is sometimes required to assess a single building or linear infrastructure. Nonetheless, the EGMS is certainly a powerful resource to spot areas of ongoing deformation over an entire city, region, or country.

The presentation will showcase some EGMS use cases to convince existing and new users about the added value of the product in enabling the identification of areas prone to instability, the improvement of decision-making processes, the prioritization and cost-saving of, and the implementation of targeted land-use regulations.

Nowadays, urban environments encounter a plethora of diverse challenges and threats due to the effects of Climate Change (CC). The European Cities, are constituted as significantly sensible environments with high susceptibility to threats such as increased temperatures (Urban Heat Island Effect), air quality degradation, extreme weather phenomena, urban flash floodings and an overall increased risk of public health issues. To this end, Earth Observation (EO) products and applications serve a pivotal role in the timely monitoring of urban environments by providing an analytical perspective of several urban-related variables. The combination of EO with novel digital tools and Decision Support Systems (DSS) could play a determinant role in assisting decision-makers by means of providing access to relevant data and insights. The Horizon 2020 research and innovation project entitled “Development of a Support System for Improved Resilience and Sustainable Urban areas to cope with Climate Change and Extreme Events based on GEOSS and Advanced Modelling Tools - HARMONIA GA 101003517” introduces a series of innovative digital tools along with a novel urban planning DSS that exploits the Harmonia multiparametric risk assessment methodology for a spectrum of different urban perils to eventually offer comprehensive and tangible urban recommendations for mitigating future hazard-driven adverse impacts. The proposed solution can exploit dynamically updated EO services for a series of urban perils and is offered as a web-based application with a user-friendly interface, capable to handle and visualize multidimensional (4D) geospatial information. The overall methodology and the capabilities of the system are demonstrated in four different and diverse European urban environments, i.e., the cities of Milan, Piraeus, Sofia, and Ixelles by prioritizing tangible recommendations for the most effective mitigation strategies.

Local Climate Zone (LCZ) classification and satellite image processing for mapping Urban Heat Islands (UHI) offer promising prospects for improving the cities resilience to climate change by identifying sensitive areas and proposing recommendations for urban greening strategies and promoting a more efficient and sustainable approach to urban planning.

Our prototype of LCZ generation is based on a method combining the vector approach developed by Olivier Montauban at city scale in Strasbourg in 2019 and the LCZ classification algorithm of Cerema (Cerema Sat’ 2021), after having evaluated all the needed data at the level of Aix-en-Provence and Florence. To ensure reproducibility, accessibility, and availability of data in both study areas, the retrieving of morphological indicators and Land Use and Land Cover relies mainly on raster and vector data in Open Source except for Pléiades data.

Using satellite images from Landsat 8, Aster, and MODIS, we analyzed the land surface temperature (LST) and UHI during summer (June to August) 2022 and 2023 in the conurbation areas. The objective was to evaluate the impact of these variables on the variability of urban hot spots (UHS) and on the level of thermal comfort, using the Urban Thermal Field Variance Index (UTFVI) in each type of Local Climate Zone.

This study demonstrates the complex links between LCZ, LST, UHI and UHS. The spatio-temporal evolution of LST provides information on areas that are particularly likely to become UHI in the future.

The Urban Atlas product of the Copernicus Land Monitoring Service help monitor and understand urban areas and support effective urban planning and policy making.

The service is enhancing the Urban Atlas suite of products for the reference years 2021-2024.

The upgraded suite will include the traditional land cover and land use (LC/LU) status and change layers 2018-2021 and 2021-2024 and Building Block Heights (BBH) for 2021 and 2024.

A new integration within the Green Urban Areas class will be applied on the LC/LU product for 2021 and 2024, and it will allow users to distinguish between public and private green spaces using an innovative approach based on available in-situ and ancillary data. The Street Tree Layer product will continue to be part of the product suite but for reasons of consistency it will be extracted from the Small Landscape Features component of CLMS.

The methodology applied for the creation of the new products involves integrating Sentinel-2 time series for extracting information on change and basic land cover classification and comparing the resulting product with Very High Resolution data for detecting small changes and precise outline. As fully automated, the methodology allows for a reduction of the update cycle from 6 to 3 years.

The distinction of the public vs private character of Green Urban Areas will allow the benefits of inner-city green areas for urban ecology and quality of life to be properly assessed, overcoming a limitation in the previous Urban Atlas concerning the determination of whether a green urban space is public or private. This new layer is also a direct response to requirements expressed by users in the preparation for the 2021 update.

The upgraded Urban Atlas products are designed to address the critical need for up-to-date, detailed information on urban expansion, land use changes, and environmental shifts within urban areas.

In addition to the technological advancements, CLMS is committed to ensuring these products are accessible and beneficial to a wide range of users. To ensure this, the project includes a robust user engagement program, featuring training webinars, presentations at external events, and a dedicated Helpdesk for support.

The World Bank is interested in conducting least-cost electrification studies in developing countries with a view toward universal electricity access. Accurate and up-to-date knowledge of existing electrical transmission grid infrastructure is required for this purpose. To improve the quality of this data NEO has developed a novel

smart-tracing algorithm to detect and trace electrical towers in Very High Resolution (VHR) satellite imagery. This smart-tracing approach uses existing open datasets alongside a deep learning model for object detection. The method is scalable and adaptable to arbitrary regions with satellite image coverage.

The method makes full use of existing open datasets such as nightlight satellite imagery and land use map etc. as inputs to derive probability map of transmission grid presence. This probability map guides the smart tracing algorithm to search and determine the power tower tracing direction. The power tower detection is done by a well-trained deep learning model. The method is designed to be adaptive to input imagery and be flexible to handle regional difference in terms of landscape variation and dataset availability, which makes it highly feasible to replicate to other regions in the world provided satellite imagery coverage.

In an era marked by rapid urbanization and escalating environmental challenges, the imperative for sustainable urban development is critical. Central to this challenge is the strategic development of urban green spaces, serving as city lungs and enhancing urban resilience and community well-being.

"Let’s Park", a Greek NGO, leads this movement, advocating for the creation and expansion of urban green areas. It distinguishes itself by engaging a diverse array of stakeholders, from individual citizens and community groups to businesses and local government bodies. Utilizing a bottom-up approach, "Let’s Park" leverages participatory design and co-creation, alongside modern technologies like satellite remote sensing, artificial intelligence, and advanced web technologies, to drive its initiatives.

The organization's efforts unfold through two main avenues. Firstly, it has launched a comprehensive crowdsourcing web platform connecting citizens and initiatives with municipal authorities and company ESG departments. This platform fosters a seamless exchange of ideas, resources, and support, ensuring projects are community-led and aligned with environmental and social objectives. Secondly, "Let’s Park" provides Earth Observation (EO) services, such as Land Use/Land Cover and urban sprawl mapping, crucial for urban planning and decision-making, enabling effective monitoring and impact assessment of urban parks.

Integrating crowdsourcing with EO technologies, "Let’s Park" enhances urban environments and redefines urban planning and environmental management paradigms. This collaborative approach advances the urban greening agenda and aligns with global sustainability goals. "Let’s Park" serves as a scalable, replicable model for developing resilient, healthy, and livable cities in the climate change era, setting a benchmark for future urban development worldwide.

Persistent Scatterer Interferometry (PSI) is a well-established technique in the field of multitemporal A-DInSAR (Advanced Differential Synthetic Aperture Radar Interferometry). The strength of PSI lies in its ability to detect and monitor ground displacements over large areas with sub-centimeter accuracy. However, the effectiveness of PSI in identifying deformation phenomena depends on the available PS density, which is related to the sensor resolution and site-specific characteristics.

To overcome the inherent limitation of the PSI technique, our study proposes an integrated analysis that combines displacement information extracted by multi-band SAR satellites for evaluating landslides in the Central Apennines, Italy. This area, affected by the 2016-2017 seismic sequence, requires a comprehensive understanding of landslide-related deformations and their interaction with urban areas to provide valuable insights for risk assessment and mitigation strategies. Based on the strain tensor, the developed data fusion method combines data with different orbital geometries to obtain synthetic datasets characterized by the integrated deformation velocities along the horizontal and vertical components. Compared to the single-sensor PS approach, the method identifies deformation patterns that might not be visible from an individual data source, grouping data with similar movement patterns over time to increase spatial coverage and reinforce information content. The multi-sensor analysis provides insights into the underlying causes of the processes by identifying areas experiencing similar deformation.

Landslide hazard data were combined with buildings’ vulnerability and real-estate market value to achieve a comprehensive risk assessment. Structural resistance estimates retrieved physical vulnerability, while the economic value was calculated through official government estimates of asset market values. By combining multi-sensor hazard data with asset market value and vulnerability estimates, our study aims to provide an integrated approach to landslide risk analysis, enhance disaster resilience, and inform urban planning practices of the selected test sites.

Due to the Sustainable Development Goals (SDGs) being designed at both national and globally applicable level, it is challenging to adequately reflect the local context and characteristics in different urban regions without fully utilising big earth data. To effectively address this issue, this work localized 73 indicators for the 13 SDGs, and utilizing big earth data, conducted a comprehensive assessment and prediction of the urban sustainable development status in 18 cities in Hainan province from 2010 to 2030. Our analysis specifically focused on indicator score, goal score, SDG index, SDG spatial spillover effect, and trade-offs and synergies. The results indicated an overall upward trend in sustainable development in Hainan province, predicting achievement of the SDGs by 2030. The SDG index score and spatial spillover effect showed a pattern of 'high in the north and south, low in the middle'. Although the SDG synergies are generally stronger than trade-offs, the trade-off effects develop at a faster pace. More specifically, the average SDG index of each city increased from 29.85 in 2010 to 60.09 in 2018, with a projected score of 89.76 by 2030. During 2010-2018, the synergy-to-trade-off ratio declined from 3.91 to 1.84, driven by a trade-off growth rate 2.03 times higher than the synergy. Based on the SDGs imbalances, governmental policies should foster essential transformations across sectors for sustainable development. Our work provides a valuable localized case method, and data support for monitoring sustainable development at the global urban level.

Characterizing Built-up Areas (BA) is crucial in making cities and human settlements safe, resilient, and sustainable, supporting the Sustainable Development Goal (SDG #11). Synthetic Aperture Radar (SAR) data is a potent resource for BA mapping due to strong coherent backscatter from diverse human-made targets, distinct texture patterns, and sensitivity to its geometric characteristics. With the advent of the Sentinel-1 C-band SAR mission, dual polarimetric (dual-pol) SAR data has been widely exploited for several land cover applications. These data sets provide wide-swath SAR images at an impressive spatial and temporal resolution with a distinct cross-pol response of BA rotated to the radar line of sight (LOS).

This study exploits these advantages of dual-pol SAR data by introducing a set of descriptors that are helpful in the enhanced characterization of BA. (1) a built-up index from Single Look Complex (SLC) and Ground Range Detected (GRD) dual-pol SAR data: DpRBIS and DpRBIG, (2) a target characteristic parameter from dual-pol SLC and GRD SAR data: α(S) and α(G), and (3) scattering power components from dual-pol SLC and GRD SAR data: Pd−l, Pu, and Ps−l.

The descriptors obtained from SLC SAR data are capable of characterizing different types of built-up areas in diverse scenarios, overcoming the significant challenge of inaccurate identification of BA or buildings oriented to radar LOS and mixed BA. However, descriptors derived from GRD SAR data may pose certain challenges in identifying the oriented and mixed BA. The DpRBIS and DpRBIG range between 0 and 1, with BA having contrasting high values than other land cover targets. Similar significant variations between the values of α(S) and α(G) is observed for built-up (close to 90◦) and non-built-up areas. Likewise, we observe that the “dihedral-like” (Pd−l) power component predominates over built-up targets, facilitating its discrimination from other land cover targets.

The ERC Synergy (ERC-SyG) Project urbisphere aims to forecast feedbacks between weather/climate and cities, by exploiting new synergies between spatial planning, remote sensing, modelling and ground-based observations, and incorporating city dynamics and human behaviour into weather and climate forecasts/projections. The urbisphere field campaign in Berlin, Germany, provides new information on the impact of cities on the urban- and regional-scale boundary layer using data measured across a wide range of scales during the course of a full year (Autumn 2021 to Autumn 2022).

During an intensive thermal infrared (TIR) observation campaign in August 2022, sensors included five TIR cameras (Optris 640 Pi and Optris400 Pi) mounted on the ground and a building roof, SatelliteVu MIR (Mid-Infrared) sensor mounted on an aircraft, and Anafi Parrot Thermalsensor mounted on an UAV (Unmanned Aerial Vehicle). These measurements were complemented by satellite observations from Sentinel-3 SLSTR, MODIS, ASTER, ECOSTRESS and Landsat. Thus, data from the intensive observation period (IOP) offer a wide range of spatial resolutions (<1 m to 1 km), many collected over the same location and many at the same time. The sensors differ in their respective fields of view, their wavelengths, and their accuracies. In this contribution, we provide an overview of the TIR and MIR observations, their spatial and temporal coverages, and initial results for evaluating the spatial and temporal variability of surface temperature during the IOP.

Acknowledgement

This work is part of the urbisphere project (www.urbisphere.eu), a synergy project funded by the European Research Council (ERC-SyG) within the European Union’s Horizon 2020 research and innovation program under grant agreement no. 855005. Special thanks to the Chair of Climatology at Technische Universität Berlin for providing equipment, ensuring access to observation sites and to all those who contributed to the field work: Fred Meier, Kai König, Josefine Brückmann.

In the context of aging infrastructure, limited funding for comprehensive inspections, and the escalating risks associated with extreme weather events, evaluating the structural integrity of numerous bridge assets is a challenging task for transport managers. Recent progress in Synthetic Aperture Radar (SAR) satellites and Interferometric SAR (InSAR) techniques has led to cost-effective and high-quality measurements of infrastructure deformations. Specifically, Multi Temporal (MT) InSAR can detect displacements at the millimetre level, providing monitoring data comparable in accuracy with conventional terrestrial methods. Moreover, MT-InSAR offers broader spatial coverage, frequent updates, operates in all weather conditions, provides day-and-night acquisitions, and allows for retrospective monitoring. As a result, MT-InSAR holds the promise of complementing traditional methods for assessing bridge conditions, particularly in regional-scale evaluations. However, integrating MT-InSAR observations with an understanding of structural behaviour remains challenging due to SAR's inherent geometric differences from traditional monitoring systems. To address this, we propose a method that considers anticipated asset motion and its alignment with the satellite flight path to assess MT-InSAR sensitivity towards expected displacement directions and establish specific damage indicators. The proposed method is used for bridge analysis in the Netherlands, utilising displacement data derived from a temporal series of 3-m resolution TerraSAR-X imagery. Findings can enhance our understanding of structural behaviour and aid in proactive maintenance, ultimately contributing to more resilient infrastructure systems.

The European Commission (EC) Knowledge Centre on Earth Observation (KCEO) undertakes deep dive (DD) assessments on selected EU policy areas. DD methodology consists of the following steps: 1. definition of key actors involved, 2. collection and assessment of needs of policy Departments across the entire policy cycle, 3. Earth observation (EO) value chain analysis, 4. translation of policy needs into technical requirements, 5. fitness for purpose analysis of existing products and services and 6. gap analysis and final report production including recommendations and relevant information to contribute to filling the knowledge gaps. One of the DDs that KCEO is currently undertaking is on urban climate change adaptation. As a result of co-design activities carried out with policy Departments, four use cases were identified:

1. the role of urban greening for managing flood and drought risks and mitigating the urban heat island (UHI) phenomenon, in relation to the Biodiversity Strategy for 2030, proposed by DG ENV

1. monitoring temperature and its impacts (floods, drought and UHI) at a temporal and spatial resolution adapted to Cohesion Policy 2021-2027 reporting, proposed by DG REGIO

1. coastal flooding, coastal eutrophication, coastal erosion and geomorphological change and coastal marine heat waves, in relation to the Marine Spatial Planning Directive, proposed by DG MARE

1. compact cities and function of cities in strategic corridors in Africa and measuring and mapping urban vulnerability to make cities resilient in EU partner countries, to support the Urban Development Technical Facility in the context of Global Gateway, proposed by DG INTPA

In addition to the use cases, this DD touches upon the relevant EU Missions and defines a set of indicators for urban climate change adaptation, defining the role of EO, the related Key Type Measures and risk components and considering both the climate change impacts and adaptation action outcomes.

Nature-based solutions in urban environments can provide cooling effects, decrease air pollution, and improve mental health, amongst others important ecosystem services and health-related benefits. Ambitious plans, such as the pledge to plant 3 billion trees in the EU, the European Green Deal, or the Green City Accord support this direction. Their implementation, however, requires transformative changes on the ground to overcome business as usual approaches. The Urban ReLeaf project delivers change by bringing public authorities and citizen groups together to shape urban green infrastructure actions. Six pilot cities co-create citizen-centric innovations for the democratisation of urban greenspace monitoring and the wider policy making process in pursuit of urban climate resilience. This presentation showcases the stories of the six cities and their approaches to designing citizen-powered and multi-sensor data ecosystems to support decision making. Athens is undergoing a greening transformation with a new tree registry the combines Earth Observation (EO) and crowdsourcing methods to provide critical data for better management of greenspaces. Cascais engages citizens in sharing perceptions and thermal comfort levels while using greenspaces to validate the effectiveness of its parks. Meanwhile in Dundee, a city facing increasing grey infrastructure in deprived areas, actions to enhance the accessibility of greenspaces are co-developed with citizens and stakeholders. Mannheim has a heat action plan to safeguard its most vulnerable residents but has identified critical data gaps. Citizen observations of trees and thermal comfort, when integrated with EO and official data streams, will aid the delivery of climate adaptation measures. Riga engages diverse audiences to address concerns about air quality and greenspace usage, through the use of low-cost sensor networks. Finally, in Utrecht, data on temperature, humidity and heat stress, collected by and for citizens, will help them reduce the urban heat island effect and shape effective mitigation strategies.

In urban environments, nature-based solutions and the integration of natural capital into planning are crucial to creating climate resilient cities. These strategies act as carbon sinks, improve biodiversity and overall well-being, providing cleaner air, reducing heat and mitigating flood risk. Incorporating these elements into urban planning ensures sustainable development and climate resilience, highlighting the importance of enhancing and protecting our urban ecosystems and forests.

Lobelia Earth responds to these environmental challenges with an innovative platform that leverages web technology, satellite data from various sources (optical, radar and LiDAR) and climate science. Through big data analysis methods and the application of artificial intelligence and landscape ecology analysis methods, we measure key indicators such as soil moisture, aboveground biomass (AGB), phenology, fragmentation and connectivity. This multifaceted approach permits an accurate assessment of urban biodiversity and natural capital, facilitating informed decision making for sustainable management and climate change mitigation in urban and peri-urban forests.

These efforts are essential to address environmental degradation and promote sustainable growth that respects the limits of our natural resources. Effective measurement and management of human impact on ecosystems is vital to mitigate deforestation, ground degradation and biodiversity loss. Integrating natural capital into corporate and economic decisions permits sustainable practices that benefit both the medium and economic development, contributing to the global fight against climate change and ensuring a sustainable future and responsible environmental management.

HeatScape Resolve offers a comprehensive solution for recognizing and predicting urban heat islands (UHI) using Earth observation (EO) data, benefiting real estate developers and municipalities. It operates through three key stages: assessing the current state of UHI, simulating UHI changes post-urban development, and validating UHI post-development. This process yields localized predictions of UHI intensity tailored to specific local climate zones (LCZs), aiding in reducing building cooling energy loads and facilitating sustainable urban public space planning through detailed microclimate mapping. The study elucidates how EO-derived urban scape attributes inform inputs for predictive simulation models and outline user requirements for the service. Additionally, it will demonstrate the integration of EO aided predictive UHI services for city-scale sustainability assessments.

The commercial development activity is performed under a programme of, and funded by, the

European Space Agency and is carried out under the ARTES BASS programme.

Launched in October 2023 and funded by ESA's FutureEO programme, the Synthetic Data for Earth Observation (SD4EO) project wants to pioneer the use of synthetic satellite imagery to address a critical challenge in Earth Observation applications: the scarcity of comprehensive, accurately labelled reference datasets. The project, led by GMV in partnership with the University of Valencia, leverages advanced Computer Graphics-based simulation techniques and generative Artificial Intelligence (AI) to generate high-quality synthetic satellite imagery that mirrors real-world data with precise annotations. This innovative approach aims to substantially minimise the need for time-consuming manual labelling, while enhancing the precision and availability of reference data for training AI models in EO applications.

The SD4EO project addresses three use cases, two of which are related to sustainable urban development challenges. The first regards the human settlements categorisation for energy consumption monitoring and management. By producing synthetic images that accurately represent various urban structures, SD4EO wants to contribute to a more effective global energy demand mapping using Sentinel-2 imagery. The second use case focuses on the monitoring of photovoltaic panels. As solar energy becomes increasingly integral to urban landscapes, there is a growing need for up-to-date and accurate data on residential solar installations. The project generates synthetic imagery of photovoltaic panels under varying conditions to assist in detecting and evaluating their status. This initiative is beneficial for optimising solar energy use, informing energy policy, and managing power grid needs.

In addition, the SD4EO project aims to encourage cooperation between the AI, Computer Graphics, and EO scientific communities. The synthetic datasets and the associated code will be made available under open-friendly licences to foster innovation. This pragmatic strategy seeks to streamline EO analytics, setting a precedent in using synthetic data to overcome the limitations of conventional EO data annotation and analysis techniques.

Responding to global warming and adapting to climate change effects such as heat waves and drought is a key priority of European and national-level Climate Change Adaptation strategies. Regional and city administrations aim to reduce climate change-related health risks and increase human well-being through adequate planning measures such as establishing green and blue infrastructure. Changes in land use (LU) and land cover (LC) play an important role in determining local climate characteristics. Urban Climate, for instance, differs from the surrounding natural areas, showing higher air and surface temperatures, known as the Urban Heat Island Effect, mainly related to changes in the surface radiative properties. By leveraging LULC data, Sentinel 2 data, meteorological data, climate models and other auxiliary datasets and integrating Land surface temperature (LST) stemming from ECOSTRESS, we developed a multi-sensor/data multi-resolution downscaling algorithm grounded in the physical representation of LST [1, 2].

Our methodology leverages a super-pixel Convolutional Neural Network (CNN) architecture in two pivotal steps: firstly, modelling LST at its sensor-specific resolution to create a dense time series (e.g., 70m in the case of ECOSTRESS), secondly, the further refinement and downscaling to 10m resolution. Employing the GHSS2Net-derived model, optimized for a 5x5 super-pixel input, using 2x2 convolutional kernels without pooling layers to preserve contextual information, our downscaling approach achieves significant advancements in spatial prediction accuracy (R² ≳ 0.9) without sacrificing temporal consistency [3]. This efficiency is attributed to the model's design, which prioritizes contextual information retention through its unique convolutional structure and employs dropout regularization and batch normalization to enhance performance.

The integration of downscaling techniques into spatial planning activities while considering various climate scenarios provides a novel avenue for assessing and visualizing the impacts of urban development and LULC changes on local climates. By enhancing the resolution and accuracy of LST and climate data, our methodology supports the development of targeted, evidence-based urban adaptation and resilience strategies and facilitates policy development and communication with the broader public.

Our contribution would be in poster format.

[1] Matzarakis, A., Rutz, F. & Mayer, H. Modelling radiation fluxes in simple and complex environments: basics of the RayMan model. Int. J. Biometeorol. 54, 131–139 (2010).

[2] Rigo, G., Parlow, E. Modelling the ground heat flux of an urban area using remote sensing data. Theor. Appl. Climatol. 90, 185–199 (2007).

[3] Corbane, C., Syrris, V., Sabo, F. et al., Convolutional neural networks for global human settlements mapping from Sentinel-2 satellite imagery. Neural Comput & Applic (2020).

Cities face many challenges in terms of health, well-being and liveability. In order to make the right decisions municipalities, citizens and other stakeholders require good quality information on our habitat. Remote sensing data are very suitable for deriving both complete and up to date information. NEO has developed standardized services to monitor the urban habitat. These services provide object-based information on trees, pavement and buildings, on a national level and are an addition to the available toolboxes such as the Copernicus Land Monitoring Services. The services provide essential elements for the effective application of a digital twin approach.

Using laser altimetry data, aerial imagery, satellite data and AI techniques a national tree register has been developed that contains all trees in the Netherlands. The added value is that the registry is more complete and up to date than other registries. This completeness is important for addressing climate and heat stress issues. The registries are kept up to date using multiple acquisitions per year of very high resolution optical satellite imagery (a.o. Pleiades Neo).

With this portfolio of services actionable insights for climate adaptation and liveability are derived. Insights into buidling density, proximity of trees and nearest green area (including distance, per building) and coverage of tree crowns per neighbourhood are indicators that serve policymakers in assessing liveability. The monitoring capabilities of the services enable monitoring indicators and policy outcomes in the future (or the past, using historic data).

The services are also applied by other stakeholders involved in managing urban affairs, such as utility companies, contractors and engineering companies. Good quality data helps improving infrastructure and public space whilst preserving the trees as important elements in a healthy habitat.

The standardized services have international potential and are in the process of being applied internationally, e.g. France.

Urban green infrastructure plays a pivotal role in climate regulation by offering various ecosystem services. One crucial metric in understanding human thermal exposure is the mean radiant temperature (Tmrt), which encompasses the spatial and temporal variations of radiation exposure. In the context of urban microclimate models like SOLWEIG, the accurate characterization of trees is essential, whether incorporating existing trees or assessing the cooling effects of new greenery. Currently, urban tree structures are usually generalised inside of the model due to the lack of detailed measurements and scientific knowledge about urban tree growth.

Various vegetation types exhibit distinct effects on the attenuation of direct shortwave radiation through shading. Leaf Area Index (LAI), tree height, and trunk height significantly determine shade patterns and solar attenuation. This abstract proposes the utilisation of state-of-the-art Terrestrial LiDAR Scanning (TLS) techniques to parameterize these structural properties for the precise implementation of existing trees within urban microclimate models.

This enhanced structural understanding of urban trees will facilitate the creation of more realistic tree models, allowing for a comprehensive assessment of their impact on human thermal comfort.

SOLWEIG operates as a 2.5-dimensional model, where x and y coordinates and associated attributes (e.g. height, emissivity or reflectivity) are utilised for the calculation of Tmrt. TLS allows for the highest degree of parameterisation of urban trees within the given raster environment. By conducting a sensitivity analysis on the modelled Tmrt, we will explore the impact of tree and trunk height, canopy area and volume, and radiation transmissivity of vegetation.

This research will provide valuable guidance on the TLS data collection of tree parameters essential for evaluating current cooling effects. Which in turn leads to the identification of tree species with significant cooling potential, and determining the size at which a tree substantially contributes to human thermal comfort.

The world is constantly changing and becoming more complex in all aspects. The changes have unpredictable impacts and implications at various scales. The increasing complexities in urbanized systems pose challenges in their comprehension and management. The intricate structures and large spatial scales make visualization and analysis arduous. Conventional methods may fall short in accurately representing the situation, thereby impeding detailed analysis and decision-making processes. Consequently, communities seek new synergies to access timely, updated, standardized, reliable, user-friendly, and actionable information to make well-informed decisions. There is a pressing need for innovative monitoring systems to streamline and simplify urbanization processes. It is imperative to democratize information, integrate it into society, and ensure its accessibility to all. Earth Observation (EO)-based space techniques have become more comprehensible, accessible, and dependable for analyzing Earth resources and monitoring urban environments. The outcomes of numerous application-oriented studies have been promising thus far. This research primarily addresses the requirements of public and research organizations, with a particular focus on utilizing Imaging Radar Systems for spatial feature extraction and remote mapping of sensitive areas in Turkey. The major concern is the expanding urbanization. The study's multidisciplinary approach incorporates EO technologies, particularly integrating SAR and optical satellite imagery data to produce detailed maps of land surface features. The study primarily aims to monitor and delineate spatial features of urban developments to enhance understanding of environment, land and sea changes including land use, and analyze their connections to reference data. The basic methodology involves detecting, delineating, recognizing, identifying, and interpreting urban features. Leveraging 3D visualization, color manipulation, and the three-dimensional sensing capabilities of Synthetic Aperture Radar (SAR) data sets facilitates a better understanding of the surface, aiding in discriminating, locating, and mapping meaningful spatial information in the study area. The integration of spatially enhanced SAR and optical imagery data yields significant combined analysis results, providing highly acceptable outcomes for operational use, even in cases where extensive ground studies have been conducted. The imagery data taken from different wavelength bands are used in models that explain the processes controlling the development and configuration of new land surfaces in the region. The SAR and optical data results demonstrated the links between surface developments and remote sensing in the visible, infrared, and microwave spectra. By combining SAR and optical imagery data, land and sea surface features, objects, structures, patches, and changes are effectively mapped. Color composite analysis of SAR-enhanced images enabled optimal extraction of spatial information in the study region. Multi-seasonal and multi-year color composite SAR images highlight changes by displaying them in different color tones. EO-based systems facilitate timely identification, enabling proactive intervention, preventive measures, and documentation of urban development. This approach enhanced the precision and effectiveness of change detection. Multi source imaging systems serves as valuable data for Geographic Information Systems (GIS) analysis and can be a crucial data source for local and state governments, real estate companies, financial businesses, and individuals to make informed decisions. The integration of Space based EO multi source data offered novel information and innovative aspects for land and sea surface mapping studies. As a conclusion, EO systems play a pivotal role in encouraging research and user community activities in the vital domain of urban development mapping and change detection.

The nexus between growing urban population, increased water consumption and exacerbation of land subsidence is well understood. Interferometric Synthetic Aperture Radar (InSAR) data are effective in providing a reliable quantification of the resulting deformation at surface. However, less explored is how such satellite-based information can combine with authoritative statistics on groundwater usage and contribute to improve management practices.

In Mexico, several hotspots of land subsidence are well-known, but a country-wide mapping exercise was not available yet. Furthermore, the aquifer-system management reports issued by the National Waters Commission (CONAGUA) are a valuable resource for understanding the impact of groundwater exploitation. Using ~1700 Sentinel-1 IW SAR images acquired in 2019–2020, we perform the largest ever-made InSAR survey of land subsidence over Central Mexico. Our 700,000 km² study area encompasses the whole Trans-Mexican Volcanic Belt and several major states, hosting >85.2 million inhabitants (i.e., ∼68% of the Mexican population).

Using the parallelized Small BAseline Subset (SBAS) multi-temporal InSAR approach in ESA’s Geohazards Exploitation Platform (GEP), we estimate present-day subsidence rates for ~35.7 million coherent targets and identify yet unmapped and well-known hotspots, e.g.: −45 cm/year in Mexico City. We also compute compaction volume rates at 321 aquifer-systems. These generally correlate well with CONAGUA’s modelled and/or measured groundwater deficits, extractions and storage changes.

We derive semi-theoretical relationships between groundwater balance parameters and land subsidence for the whole Central Mexico and its main hydrological-administrative regions, which enable the assessment of ground compaction rates and volumes resulting from groundwater exploitation, and thus can inform groundwater management strategies towards climate change adaptation and future needs of a growing population. We finally discuss how InSAR-derived subsidence risk maps produced at city level prove valuable to help regional authorities in quantifying properties and population at risk.

Full paper: Cigna & Tapete 2022, Geophysical Research Letters, 49(15), https://doi.org/10.1029/2022GL098923

The Urban Heat Island (UHI) refers to the warmer temperatures experienced by a city compared to its rural surroundings. The Local Climate Zones (LCZ) concept is a suitable description of local scale landscape types used to study the UHI. This description was first published by Stewart & Oke in 2012. This approach allows UHI studies to be more comparable, regardless of prevailing local urban planning, building materials, city size and geographical location. Based on this concept, Cerema has developed, through research projects, the SatLCZ methodology, which is now operational, to determine the vulnerability of urban environments during summer heat waves. SatLCZ uses very high resolution (VHR) satellite images from the Pleiades and SPOT missions. This method, which can be replicated in any city, divides areas into homogeneous elementary typo-morphological units based on their climatic behaviour. The SatLCZ maps will enable local actors to better understand how their territory reacts to the UHI in order to implement solutions in their urban fabric: de-pollution, renovation of the built heritage, planting of vegetation, adaptation of mobility, etc. Moreover, to better support public planning policies in their fight against the urban heat island phenomenon, LCZ mapping can also provide basic indicators such as imperviousness and vegetation cover, but also, if sufficiently detailed data are available, a socio-economic vulnerability index. Finally, this method has allowed the production of a national map covering French cities with more than 50,000 inhabitants. The SatLCZ is now an operational tool that can be used with QGIS3 modellers and is available on the Cerema GitHub

The ONEKANA project addresses the urgent issue of thermal inequalities in African urban areas, exacerbated by climate change. Using Earth observation (EO) technology, including advanced EO/AI models such as Machine Learning and Deep Learning, the project examines the disparate exposure of urban populations in urban Africa, to varying temperatures and extreme heat. By leveraging accessible satellite imagery sources such as Sentinel, Landsat, MODIS and Ecostress, ONEKANA ensures the adaptability and scalability of its methodology in diverse urban contexts. Preliminary results have revealed significant local variations in thermal exposure, delineating clear spatial patterns of heat vulnerability. Simultaneously, the project is pioneering the mapping of slum areas with a systematic approach that fuses open-source EO imagery with unsupervised learning techniques. This method produces refined and up-to-date maps of urban deprivation, circumventing the gaps and unreliability often associated with traditional manual slum delineation. Early analyses highlight the potential of image-derived morphometrics and texture indicators to accurately identify patterns of deprivation. Furthermore, the project advances in the modelling of the distribution of population within the slums. The collection of accurate in-situ population data in Nairobi challenges and revises existing population estimates, significantly improving the accuracy of urban population maps. When combined with thermal exposure assessments, this dataset aims to locate populations most at risk due to thermal disparities. ONEKANA not only enriches the scientific discourse on urban thermal inequalities, but also introduces a replicable framework applicable across diverse urbanities in the Global South. The ultimate goal of the project is to integrate EO-derived insights into urban planning and climate adaptation efforts, thereby strengthening urban resilience to climate-induced thermal extremes. Through scientific rigour and user-centred methodologies, ONEKANA lays the foundation for transformative urban planning that prioritises the well-being of the most vulnerable urban populations.

The assessment of long-term urban transformations is critical for informed decision-making and scientific support during urban reconstruction. While commercial Very High-Resolution imagery is often employed for sustainable urban development planning, Copernicus Sentinel-2 data offer a valuable alternative, providing free images at frequent revisit times. This study investigates the feasibility of using Sentinel-2 data to analyse long-term changes in urban buildings following seismic events. Given Sentinel-2’s spatial resolution limitations for clear identification of alterations, our methodology relies on temporal analysis of a parameter known as Perceived Lightness (PL). PL is derived from Red-Green-Blue reflectance values and captures variations in luminance caused by events like building demolition, reconstruction, or rubble removal. To address PL variation due to vegetation and seasonal changes, our analysis incorporates the Normalized Difference Vegetation Index (NDVI), which assesses green vegetation health. By comparing the PL trend of the building under investigation with a reference PL trend, anomalies are revealed, potentially indicating a change, and defining a possible date of change.

The methodology is applied to Sentinel-2 data from L'Aquila and Amatrice, two central Italian cities struck by earthquakes in the past. The results are promising, and, beyond the post disaster assessment, this methodology can also be employed to track urban development and sprawl over time. Furthermore, it can be potentially adapted to analyse changes in other types of land cover, such as deforestation. The ability to monitor these changes using freely available data can be a valuable tool for environmental monitoring and conservation efforts.

Extreme weather events, sea level rise and coastal erosion are major issues that need to be urgently addressed in European coastal cities. Smart technologies can support Coastal Cities Living Lab (CCLLs) in detecting potential risky conditions in the study area and co-designing adaptation solutions, like Nature Based Solutions (NBS) and Ecosystem-Based Adaptation (EBA). A GIS-based Early-Warning Support (EWS) system and Digital Twin (DT) platform presented here can be deployed in the CCLLs to assist both the planning of long-term climate resilience strategies and decision making in case of risky events. We show how the the effectiveness of these solutions is evaluated by exploiting multi-temporal flood maps obtained from satellite in the coastal area of Massa (Italy). Specifically, we are developing a general analysis tool for testing outputs of hydraulic and hydrological models included in the DT exploiting synthetic aperture radar (SAR) images such ad Sentinel-1 or COSMO-SkyMED, or optical imagery like Sentinel-2 available for major flood events in the study area in the last decade. Information on real case studies are retrieved by municipality report archives, Floods Directive maps, local newspapers and exploration of regional agencies website and Copernicus Services. Flood hazards maps in terms of flood extent and, wherever possible, water depth or flow velocities distributions, simulated by the models implemented in the DT will be thus compared to real flood events mapping and validated. Evaluation of the effectiveness of the procedure and solutions and their possible incorporation in environmental monitoring, early-warning systems, and decision-making processes at different CCLLs will be then assessed through meetings with relevant users. This work is supported by the project SCORE (Smart Control of the Climate Resilience in European Coastal Cities), funded by the European Commission’s Horizon 2020 research and innovation programme under grant agreement No. 101003534.

Climate change will increase the number, duration and intensity of heatwaves. Urban heat islands amplify these climate extremes, further increasing heat stress in cities. Nature-based solutions, like increasing green/blue infrastructure in cities, are proposed to mitigate heat stress by enhancing evapotranspiration. However, it is unknown how urban vegetation and related carbon, energy and water cycles will respond to climate change and how it influences the potential of nature-based solutions to mitigate urban heat stress.

Remote sensing facilitates monitoring of vegetation state and environmental conditions at large scales. Studying urban vegetation, however, requires spatially high-resolution estimates of abiotic and biotic factors. These factors are essential for the parameterization of models to simulate vegetation-mediated processes (e.g. evapotranspiration, gross primary production), or monitoring vegetation health. The required high spatial resolution imposes substantial methodological challenges to cope with the large heterogeneity of urban environments.

Particularly cast shadows and geometric-optical scattering significantly constrain the retrieval of abiotic and biotic factors. If not properly accounted for, shadowed pixels can for example show a decrease of up to 25% in the NDVI compared to fully illuminated pixels. This leads to an underestimation in vegetation health studies or in modelling evapotranspiration products.

Our study aims to demonstrate and quantify the effects caused by shadowing on the retrieval of abiotic and biotic factors from spatially high-resolution data. We particularly focus on leaf area index and Absorbed Photosynthetic Active Radiation, comparing retrievals from in situ measurements, spatial high-resolution airborne data, and operational satellite data to investigate scaling effects. We discuss methodological challenges and needs related to retrieving abiotic and biotic factors in complex environments. We expect that derived insights allow moving towards advanced retrieval techniques for abiotic and biotic factors in complex urban environments and eventually improve our understanding of urban vegetation in the context of climate change.

This work describes preliminary attempts aimed at creating a dataset of daily averages of aerosol optical depth (AOD) on an intraurban scale (300m) using MODIS MAIAC AOD, SEN3 SYN, and AOD from ERA5 reanalysis models. Our preliminary efforts were aimed at understanding the quality of available AOD products by comparing them with daily average measurements provided by the AERONET network for the Italian peninsula during the reference period 2019-2023. MODIS MAIAC AOD proves to be state-of-the-art in satellite AOD reconstruction, while SEN3 SYN correlates less and shows a significant bias when compared with AERONET.

Our efforts are oriented in two directions: a) evaluating whether SEN3 SYN is mature enough to deliver unbiased AOD products on an intraurban scale on a daily, weekly, and monthly basis, possibly using other sources of information such as DEM, latlon, and LST; b) performing data imputation of missing observations using AOD from reanalysis models such as ERA5.

Regarding AOD correction on an urban/intraurban scale, we are evaluating pixel-based approaches such as linear/nonlinear/GAM regression algorithms fueled by the combined use of SEN3 SYN, MODIS MAIAC, ERA5 AOD, and auxiliary data such as MODIS land surface temperature and climatic data.

Our findings demonstrate that there is room for further improvement of AOD products by imputing missing AOD values and by further calibrating AOD using regression models fed with available AOD estimates and auxiliary data.

This work is part of a collaborative project funded by ASI and called APEMAIA (Assessment of PM Exposure at the intra-urban scale in preparation for the MAIA mission). The project is designed to investigate the potential of MAIA by developing a multi-modular system for extracting PM concentrations at the intra-urban scale using Artificial Intelligence techniques.

Building anomaly and displacement detection are critical for ensuring the safety and longevity of structures.

Based on the progress of the RepreSent project [1][2], the unsupervised building anomaly detection methods based on GNN autoencoders and LSTM autoencoders using PS-InSAR have been successfully developed and demonstrated their effectiveness in detecting three types of building anomalies caused by step, noise, and trend displacements for Rome (Italy).

The purpose of the current study is to enhance the ability to detect building anomalies. Given the varied and changing nature of urban environments, we aim to expand the area of study from Rome (Italy) to Bucharest (Romania). This expansion allows us to better understand the patterns of anomalies across different urban landscapes. By using the recently released European-wide Building Footprint Datasets in our models, we expect to deepen our knowledge of the relationship between various building attributes (e.g., construction year, height, seismic risk level) and the anomalies detected. We also plan to refine our anomaly detection by applying signal decomposition techniques to minimize prediction errors, particularly those associated with noise. Furthermore, our goal is to advance our detection methodology by not only identifying the occurrence of anomalies but also predicting their timing and duration.

The dataset focuses on Bucharest, the capital of Romania, which faces a significant challenge due to numerous buildings from the late 19th century that have structurally deteriorated over time and do not comply with current seismic standards [3]. According to the latest statistics released on March 29th, 2024, by the Bucharest Municipal Administration, over 2700 buildings are at risk of collapse in the event of an earthquake.

This work is supported by the European Space Agency with contract as part of the RepreSent project under the Grant 4000137253/22/I-DT.

References:

[1] ESA RepreSent project website, https://eo4society.esa.int/projects/represent/.

[2] Kuzu, Rıdvan Salih, et al. "Automatic Detection of Building Displacements Through Unsupervised Learning From InSAR Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2023).

[3] Ianoş, Ioan, et al. "Mapping accessibility in the historic urban center of Bucharest for earthquake hazard response." Natural Hazards and Earth System Sciences Discussions 2017 (2017): 1-24.

The purpose of this contribution is to leverage the integration of medical and Earth observation (EO) data with artificial intelligence (AI) to estimate the potential impact of future transmissible diseases, with a specific focus on cancer patients. By combining medical treatment progress and monitoring the living environment from space, this research aims to provide valuable insights into the estimation of healthcare requirements and resource allocation from various perspectives. The future aim of this research is to support local authorities in organizing effective medical schemes while enabling central authorities to develop resilient plans for future pandemics.

Both remote sensing and medical fields benefited a lot from the machine learning methods, originally developed for computer vision and multimedia. We investigate the applicability of the same data mining-based machine learning technique for exploring the structure of both Earth observation (EO) and medical image data.

EO images were acquired for a city with high diversity in term of builds, transportation infrastructure, green areas, etc. The EO dataset consists of images from multispectral and radar sensors. In addition, medical images were acquired by computed tomography (CT). The methodology has been tested by several experts, and the results were checked by either comparing them with reference data or through the feedback given by these experts in the field.

The results of such a study could help in the identification of possible causes for which in a city there is a higher number of patients with a particular disease (e.g., patients with cancer) towards another city where the number is smaller, considering the structure of the city (e.g., the surface of the green areas, number of hospitals, the surface of the industrial area, type of industry). Such a perspective could be ground-breaking in medical epidemiology.

Douala, a city situated on the coast of Cameroon in the Gulf of Guinea, is characterised by its low elevation above sea level and sedimentary geology, making it particularly susceptible to erosion, subsidence, and sea level rise. Currently, Douala City and its surrounding mangrove forests experience alarming rates of coastal erosion, frequent flooding, complete land loss, and evidence of subsidence from regional and continental research. This raises critical questions and reveals numerous research gaps, such as the need to better understand current coastal city dynamics; approaches for monitoring and predicting Douala's low coastland changes; the need to understand the rates, causes, and patterns of subsidence; and lastly, the understanding of the combined impacts of multiple factors on coastal city vulnerability. Therefore, this study aims to fill these knowledge gaps by investigating, understanding, and estimating the causes, consequences, and coastal vulnerability of land subsidence. In this study, remote sensing data, InSAR analysis, spatial analysis, and statistical analysis were used to assess the actual land subsidence rate, determine the factors influencing land subsidence, estimate the influence of land use change on subsidence processes, and establish an integrated vulnerability assessment for the coastal areas of Douala. The findings of this study indicate an average rate of subsidence amounting to 2.9 mm/year, which is indicative of subsidence occurring in all areas of the city. Furthermore, the effects of land use were observed to be dependent on the period and rate of change. These results will be of great importance in gaining a more comprehensive understanding of the dynamics of Cameroon's mangrove landscape and the susceptibility of coastal infrastructure to subsidence, coastal retreat, and potential flooding events. These findings can be utilised to develop sustainable management strategies for the coastal zone of Douala.

Natural hazards and severe weather events represent an increasing threat to both human lives and property. With climate change, extreme weather events are projected to occur more often, increasing the risks of damage to buildings and infrastructure across many regions of the world.

Earth observation satellites can play a crucial role in disaster response and management, offering unprecedented access to large-scale views of affected areas. In particular, deep learning techniques have great potential for automated building damage detection from satellite imagery. Consequently, several recent studies proposed new network architecture and demonstrated their effectiveness on the popular xView2 Building Damamage Assessment (xBD) dataset featuring bi-temporal very-high-resolution image pairs of multiple disasters. Although achieving strong performance, many of these methods are highly engineered, including the winning solution of the xView2 competition. From a practical perspective, however, there is a high demand for simple and robust methods with good generalization ability.

Therefore, our work focuses on simplifying the xView2 winning solution, keeping only the vital components while retaining accurate building damage detection performance. Thereafter, the xBD dataset splits from the xView2 competition are rearranged to eliminate spatial overlap between training and test locations. We evaluate several recent building damage detection methods on the proposed split and shed light on the limited generalization ability of existing methods under more realistic scenarios. Furthermore, we hypothesize that minor and major building damages have distinctive characteristics across different disaster types, hampering the generalization to new areas. To that end, we propose a novel method incorporating readily available disaster-type information into the building damage prediction pipeline. We empirically demonstrate that our strong baseline conditioned on disaster-type information outperforms state-of-the-art methods on the proposed realistic split of the xBD dataset.

Extreme heat endangers human health and well-being and impairs the use of public spaces. Dortmund’s Integrated Climate Adaption Master Plan prioritizes actions and measures to improve heat resilience. This project supports the city of Dortmund (Germany) in attaining this goal, by deploying a state-of-the-art biometeorological sensor network and developing a nowcasting service for monitoring thermal comfort across the city. The project aims to pioneer the integration of thermal comfort data in smart-city ecosystems and provide actionable insights for the development of Dortmund’s Heat Action Plan. Modeled, remotely sensed, and in-situ data will be used to provide near-real-time information regarding the outdoor thermal conditions. City-officials of Dortmund are involved in the design of the dashboard, and the weather station network, ensuring they meet their needs. The collected data will be used in a series of on-ground actions, supporting the evaluation of existing climate adaptation measures, and the design of new ones. These actions include the mapping of areas with high potential for planting trees, the investigation of changes in human behavior during hot days, and the assessment of backyard greening strategies. To engage with the local stakeholders, promote the role of citizen scientists, and disseminate the project, a series of workshops and on-site events are planned, such as climate comfort labs, mobile measurement campaigns, or climate walks with citizens. The overall goal of the project is for the city of Dortmund to adopt and integrate the developed network and nowcasting service into its smart-city ecosystem.

With global warming and increasing urbanization, especially tropical regions will experience seasons of extreme heat, the impacts of which require better mitigation measures from urban planners. This was especially evidenced in 2023 with the occurrence of el Nino, and related extreme temperatures for prolonged periods of time, causing amongst other problems, health challenges to the urban communities.

The European Space Agency’s Global Development Assistance (GDA) Urban project aims to foster the mainstreaming of EO into International Financing Institute (IFIs) programmes in themes such as urban resilience. In this context, a case study focused on mapping and analyzing UHIs within Sargodha city, Pakistan for an Asian Development Bank (ADB) project was undertaken. Sargodha is a densely populated city experiencing temperatures above 40^oC in the summer months. In addition, rapid urban sprawl/urbanization has resulted in city expansion, and reduced green areas.

EO data was used to derive Surface Urban Heat Island Intensity (SUHII). SUHII is an indicator for the increased local temperature due to the urbanization/imperviousness effect and allows decoupling the absolute temperature into two components. This helps in modelling urban heat. The method entails estimating the linear correlation via land surface temperature, as measured via Landsat 8, and impervious surfaces, using the world settlement footprint. Additional analyses included a time series exhibition throughout the year of 2021, creating a simple model for predicting the increased SUHII beyond the recorded temperatures and a basic greening simulation - if some areas were to be transformed into parks.

For the Sargodha use case, the datasets (vectors/city blocks) were favorably received by ADB and used in planning park locations. Future work aims to increase outreach further by building a geospatial dashboard for showcasing urban heat and SUHII, with accompanying supporting geodata (population density) and analyses in another 1-2 cities.

Global monitoring platforms are key tools to evaluate changes in urban areas and to facilitate sustainable urban development. In this context, the Urban Thematic Exploitation Platform (UTEP) is a leading web-based platform that provides thematic products and indicators to policy makers, urban planners, and other stakeholders. UTEP takes advantage of distributed high-level cloud computing infrastructures and provides multiple functionalities to enable users to easily access, visualise, and process EO data.

In order to keep serving the needs of the urban user community, the platform is currently evolving by following the “algorithm-as-a-service” paradigm following the Best Practice for EO Application Package as defined by the Open Geospatial Consortium and the EO Exploitation Platform Common Architecture (EOEPCA). This Best Practice supports developers that want to adapt and package their existing algorithms written in a specific language to be reproducible, deployed and executable in different platforms. The Application Package, encoded as a Common Workflow Language (CWL) document, comprehensively describes the full data processing application. Developers build container images that encapsulate their application and command line tool(s),which are then published on container registries for easy access and deployment.

UTEP currently supports the ingestion, metadata extraction and calibration of free and commercial EO data from more than 40 missions (Radar and Optical). EO data is exposed using the SpatioTemporal Asset Catalog, where single band assets are classified under Common Band Names.

In this work we use an algorithm for multi-mission and multitemporal analysis in urban areas to demonstrate how an EO processor can be easily integrated and deployed in UTEP. This exercise also showcases how the platform can handle massive processing using scalable cloud resources. Thanks to this evolution of the platform, UTEP offers to users a cost-effective production environment ready to host other thematic processors from researchers and service providers.

Urban areas show significant differences with natural environments regarding their effects on weather and climate through land surface processes attributed to the characteristics of urban form, function and presence of pollutants from anthropogenic activities among others. These effects include elevated surface and air temperatures, increased boundary layer height and increased CO₂ concentrations. In turn, urban elements such as trees which have biophysical mechanisms driven by meteorological conditions show altered behavior compared to their natural environment-based counterparts. These differences are conceptually well-established, however observing these and accounting for their implications is not straight-forward. In this direction, this study investigates the urban effects on tree phenology for Berlin. In particular, we compute phenology-based parameters (Start of Season, Peak of Season, End of Season) over a NWP (Numerical Weather Prediction) model grid configuration, moving outwards from the city center. A cloud based approach is implemented to achieve this, leveraging the capabilities of GEE (Google Earth Engine), using the Sentinel-2 time-series for 2023 and ESA’s World Cover product. The total area covered expands well beyond the boundaries of Berlin, in order to capture potential urban-rural differences, and explore similarities as well as diverging outcomes compared to other studies assessing relevant vegetation traits in an urban climate context.

As urban areas globally face unprecedented challenges, the importance of leveraging advanced technologies for sustainable urban planning has never been more critical. In this framework, the CITYNEXUS project is specifically designed to confront and mitigate existing urban challenges in the City of Copenhagen, such as traffic congestion, air quality, and urban livability by enabling the near-real time simulation of the effects of infrastructural, land-use and policy changes on air quality, mobility and health. Specifically, CITYNEXUS is one of the first 5 Use Cases of the Destination Earth (DestinE) Core Service Platform (DESP).

Central to the activity is the integration of diverse datasets, including crowd-sourced human mobility information gathered from smartphones, governmental and para-governmental data, Google Environmental Insights Explorer air quality data, and EO(-based) layers from the DestinE Data Portfolio. These datasets play a pivotal role in the development of state-of-the-art models capable of conducting in-depth analyses and forecasts on the potential impacts of proposed urban interventions, facilitated through robust 'what-if' scenario simulations.

While targeting the City Copenhagen, CITYNEXUS aspires to broader applicability and scalability in various urban contexts within Denmark and potentially worldwide, striving to contribute to the adoption of digital twin technology and space-based data in data-driven urban planning and environmental management. By empowering urban planners and policymakers with the ability to simulate and critically evaluate the ramifications of varied urban interventions, the project not only addresses specific urban planning challenges but also showcases a scalable solution that could inspire cities globally in the pursuit of sustainable development.

Studying changes in land cover and land use (LCLU) within urban environments provides critical insights into the dynamics of our cities. Particularly, monitoring the evolution of LCLU, such as deforestation, mining, agriculture, or other anthropogenic activities, can significantly alter the landscape and soil composition. These alterations may lead to soil erosion or instability, increasing the risk of landslides or soil subsidence, which, in turn, can damage roads and railways built in or near affected areas.

Remote sensing data, especially multispectral and multitemporal optical imagery, is instrumental in accurately monitoring LCLU changes globally. Automated approaches, particularly within artificial intelligence and machine learning, have shown impressive capabilities in identifying LCLU classes using such imagery, facilitating the study of global changes.

Planetek Italia is actively developing an operational infrastructure monitoring service to support predictive maintenance of roads, railways, and bridges. This initiative involves analysing multiclass LCLU changes (i.e. changes from urban class to other and vice versa) with time-series multispectral Sentinel-2 data and employing supervised machine learning approaches. Supported by the Italian Space Agency (ASI) under the I4DP Market project (Innovation for Downstream Preparation Market), this initiative aims to provide a comprehensive solution aligned with guidelines for risk classification, safety assessment, and monitoring of existing bridges outlined by the Italian Ministry of Infrastructure and Transport.

The proposed operational service builds upon the existing Rheticus^® Safeway, developed by Planetek within the Horizon 2020 Safeway project concluded in February 2022. Enhancements and adaptations performed during the ASI project cover both operational and technological aspects, democratizing and scaling the service to a European and global level.

Adopting this operational infrastructure monitoring service signifies a significant step toward efficient and proactive maintenance strategies. By leveraging satellite-based geo-analytics information, the service ensures the safety and resilience of roads and railways infrastructure networks on a broader scale.

The Asian Development Bank (ADB) operates through resident missions in various regions, and their development missions require access to Earth Observation (EO) products and tools to enhance the understanding of climate change impacts, natural hazards, and disasters that affect the areas they are overseeing.

Planetek Italia, with the support of ESA and ADB itself, developed customized EO-derived geo-hazard indicators useful for a better understanding of the impacts of climate change, natural hazards, and disasters in developing countries like Indonesia, Bangladesh and Papua New Guinea.

The EO-developed products offer valuable support to policy and decision-makers in several ways:

1. Providing Essential Baseline Information for the implementation of projects in developing countries by the IFI like ADB: the developed satellite EO-based geo-hazards and geo-analytics represent an innovative solution that provides new ways for ADB to address challenges in developing countries.
2. Support the geo-hazards risks assessment at the country scale: The worldwide coverage and high-revising time guaranteed by the Copernicus satellites present an unprecedented opportunity for natural hazard assessment and its continuous monitoring at the country scale.
3. Empower decision-makers: The operational use of the developed satellite EO-derived products at country scale is expected to empower the decision-makers with accurate information for:
   1. Resilient strategies implementation.
   2. To support sustainable development and disaster risk reduction.
   3. Recovery support after a natural disaster.
4. Better understanding the benefit of operational use of the EO-derived products: The EO-developed products contributed to increasing the benefit awareness connected with the operational use of Earth Observation products in development projects.

To promote the operational application of the developed EO products in operational projects, the ADB supported dedicated capacity-building activities involving the national authorities of Indonesia, Papua New Guinea, and Bangladesh in different projects where Planetek Italia was involved starting from 2018 through the ESA project EO4SD.

Asphalt roads are vital for modern societies, facilitating efficient transportation of people, goods, and services. However, these roads degrade over time due to factors like temperature, oxidation, loads, and water. To address this, a project in Milan developed a methodology using very high-resolution (VHR) satellite sensors to assess road surface aging and support maintenance activities.

The reflectance spectra of asphalt roads change as they age, enabling remote sensing to monitor pavement conditions. Yet, the spatial resolution of satellite images poses challenges, often mixing road pixels with surrounding features like vegetation and buildings. To overcome this, the project used Worldview-3 satellite images to calculate indicators related to pavement color, visibility of road markings, and material composition (spectral unmixing).

These indicators were combined to create a synthesis asphalt aging index, categorizing roads into different aging classes. However, creating a precise "road mask" to isolate areas for analysis proved difficult due to various disturbances in the images such as shadows, vegetation, and vehicles. Despite advanced techniques, some elements remained, affecting subsequent spectral analyses.

Nevertheless, field evaluations of the aging index showed good agreement, proving its usefulness for the city's road maintenance department in devising timely maintenance plans. Despite limitations, the developed methodology provides actionable information on urban road aging, facilitating efficient maintenance decisions.

In essence, the project demonstrated the efficacy of utilizing VHR satellite sensors to assess asphalt road aging, offering a practical approach to support maintenance planning. Though challenges remain, the methodology provides valuable insights for maintaining urban infrastructure effectively.

The objective of Horizon 2020 JUSTNature project is the activation of nature-based solutions (NbS), based on the principle of the right to ecological space.

Ecological and socio-economic status and disparities profiles have been created for six European cities, with the objective of guiding the strategic process of NbS planning. The profiles were built as urban units' agglomerations with similar characteristics, defined by a set of indicators representing the key challenges that NbS are intended to address. They are the six (in-)justice components enabling NbS activation: air quality, carbon, thermal, spatial and temporal (in-)justices as well as flora, fauna habitat (non-)inclusiveness.

To assess the thermal (in-)justice indicators, an AI-based methodology was developed by fusing Sentinel-2 with Landsat to obtain a 10m Land Surface Temperature (LST) monthly time-series for the period 2017-2022. A clustering algorithm was applied to the LST monthly time-series to generate a “Heat Stress map”. Besides, monthly Surface Urban Heat Islands (SUHI) within the summer months were computed. Additionally, a SUHI Likelihood map was computed by averaging the maximum monthly values of SUHI. To assess the temporal (in-)justice indicators, a multi-temporal Land-Cover map, integrated by the degree of vegetation and imperviousness, was produced for the years 2018, 2020, 2022, based on Machine Learning techniques on Sentinel-2 time-series. A clustering algorithm, namely the Hierarchical Density-based spatial clustering, was then applied to the collected indicators coupled by socio-economic/demographic data, to identify common patterns stressing disparities within the city.

EO-based data, enabling the provision of spatially continuous, accurate and regular measurements of various parameters within the urban context at both local and global scales, proved to be of pivotal importance in providing city administrators and urban policymakers with the strategic supportive information they need to plan and implement JUST and appropriate NbS solutions.

The Urban Heat Island (UHI) effect is heavily affecting urban regions, especially in the Mediterranean region due to climate change. A range of Nature-Based Solutions (NBS) have been proposed to mitigate overheating in urban environments. Up to now, NBSs have been mainly applied in experimental sites at a local-scale. At the current state, NBSs are expected to only have a local effect on heat mitigation due to their limited spatial extent. Therefore, the NBS efficiency is difficult to be adequately monitored by spaceborne Earth Observation (EO) and as a result measuring the potential benefits of the NBS remains a challenging issue that hampers their widespread application. In the framework of the EU-funded project CARDIMED, an effort is being made to upscale NBS and their related benefits at a larger scale based on earth observation techniques fused with in-situ and crowdsourcing data. To fulfil the aforementioned key objective, a multi-sensory data fusion approach will be adopted, assimilating geospatial data sources collected from in-situ, crowdsourcing and EO-based sensors. More specifically, we propose a methodology for obtaining temperature data at a higher resolution, allowing this way to locally quantify the efficiency of the applied NBS solutions in the pilot regions of CARDIMED for heat mitigation. The methodology is based on a downscaling, cross-modality framework that combines measurements from ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), Land Surface Temperature (LST) products derived from SLSTR Sentinel-3 satellite mission and in-situ data produced either from professional sensors or Citizen Science participatory campaigns. This will enable us to grasp the gap in NBS efficiency estimation, as a first step to the NBS upscaling strategy. 

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under CARDIMED project (Grant Agreement No. 101112731) (Climate Adaptation and Resilience Demonstrated In the MEDiterranean region).

Responding to global warming and adapting to climate change effects such as heat waves and droughts is a key priority of European and national climate change adaptation strategies. Soil sealing, covering of ground surfaces with impermeable materials or buildings, thereby preventing water infiltration into the soil, has a significant impact on the urban climate, especially in the context of urban-heat-islands (UHI). Administrations at different levels aim at reducing health risks associated with climate change and to improve human well-being through appropriate planning measures like policies, urban planning strategies but also technological solutions. The Copernicus Land Monitoring Service (CLMS) supports these activities with dedicated high-quality, pan-European data products.

The High-Resolution-Layer Non-Vegetated Land Cover Characteristics (NVLCC) portfolio from the CLMS enhance planning capabilities and support evidence-based adaptation measures to build resilience against climate impacts. The NVLCC’s raster layer focus on impervious areas, at 10m resolution across EEA38-countries aiding frequent land cover updates and serving as an early detection system for environmental changes. Derived from Copernicus Sentinel missions, the product includes components for imperviousness densities, built-up areas, (and newly permanent bare surfaces) that provide insights into artificial and bare surface cover and building constructions.

Spatially explicit information is essential for informing climate adaptation discussions, guiding zoning decisions and communicating potential climate change impacts and mitigation measures to stakeholders. The CLMS NVLCC data, when used alongside with various data sources like meteorological, demographic, and socio-economic data, can provide detailed and up-to-date information on land use and land cover, which is often lacking for effective planning. This information is crucial for assessing the impact of building development on local climate, analysing the relationship between building stock and green areas, and understanding of heat storages in urban areas.

With the increasing complexity of urban environments, there is a growing need for tools that provide precise and detailed insights into how different planning decisions might play out. Precision in planning helps in making informed decisions that can minimize risks, optimize resource use, and ensure the well- being of urban populations.

This study introduces a novel approach in urban planning by leveraging Generative Adversarial Networks (GANs) to generate multispectral synthetic satellite imagery, particularly tailored for nature-based solutions. The primary focus is on enhancing the capabilities of urban simulation tools in the context of sustainable urban development and climate resilience. The methodology extends upon existing frameworks by integrating advanced GAN architectures for the generation of high-fidelity multispectral imagery that mimics the characteristics of the Sentinel-2 satellite constellation, enabling simulation of various urban and rural scenarios like urban green spaces, water bodies, and agricultural lands.

The core of this methodology revolves around the collection of a comprehensive dataset of high-resolution multispectral images matched with urban and rural landscapes labels. An innovative aspect of this approach is the simulation of multispectral features specific to different local urban settings. By using historical Sentinel-2 images, the model gains the ability to replicate the unique ecological and urban characteristics of a particular area. This process ensures the generation of coherent multispectral data learning from past satellite images. Finally, the generation of synthetic satellite images stars from vector files representing various urban planning scenarios.

This methodology bridges the gap between theoretical planning and real-world application, offering a potential tool that enhances urban resilience, fosters sustainable development, and supports informed decision-making.

The aim of this work is to categorize well-being conditions of citizens in several urban contexts based on temperature and air pollution extreme values acquired from space. The proposed approach relies on the evaluation of the incidence of Urban Heat Island (UHI) and air pollution phenomena in the periods of the year in which they can potentially reveal their maximum intensity in specific metropolitan areas.

By considering the levels of tropospheric Nitrogen dioxide (NO2), with particular attention to winter season, and the Land Surface Temperature (LST) values, particularly in summer with possible cUHI formation,the areas in which the two phenomena overlap at a spatial level over the course of the year have been identified and categorized based on the intensity of such extreme conditions.

After a deep review of literature, different sensors and mathematical approaches have been considered to compute the UHI index, by evaluating the ability to recognize the phenomenon at varying spatial resolution, focusing on the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), due to its advantages in terms of spatial resolution, equal to 60m. During computations, the morphology of the city also has a crucial role: geometries of buildings and streets, presence of trees, and eventual mitigation strategies contribute to obtain the most relevant outcomes.

The air quality study has been instead settled leveraging on the results of NASA new product that provides monthly averages of tropospheric NO2 vertical column density derived from the level 2 Tropospheric Monitoring Instrument (TROPOMI) oversampled to a spatial resolution of 0.01˚ x 0.01˚(https://disc.gsfc.nasa.gov/datasets/HAQ_TROPOMI_NO2_CONUS_M_L3_2.4/summary?keywords=HAQ_TROPOMI_NO2_CONUS).

The research has been developed on metropolitan areas having high population density and consistent risks of heat waves and NO2 concentration anomalies, e.g. New York. Outcomes of the analysis are urban maps identifying different classes associated to well-being conditions for citizens considering long time period analysis.

Urban applications based on Earth Observation (EO) data are at the core of the Italian Space Agency (ASI)’s roadmap to develop downstream applications that could serve institutions to address their specific challenges and priorities in cities management. “Sustainable Cities” was indeed the theme of the first call for ideas that ASI launched in 2022 to start implementing the new “Innovation for Downstream Preparation for Science” (I4DP_SCIENCE) program.

I4DP_SCIENCE is devoted to the Scientific User Community, i.e. Italian Universities and Public Research Bodies, and is composed of joint projects with ASI demonstrating the usefulness of novel methods and algorithms to support applications of user’s interest falling within topics of national relevance, e.g. defined by the National Copernicus User Forum, and/or falling within international agendas, e.g. the UN Sustainable Development Goals (SDGs). All the demonstrations are carried out jointly with the reference users who are actively engaged since the initial user requirement consolidation and, throughout the project, via capacity building and training activities towards the user uptake.

Of the whole I4DP_SCIENCE portfolio, two projects specifically address challenges in metropolitan cities.

The LCZ-ODC project with Politecnico di Milano developed a novel workflow to produce multi-temporal and multi-resolution Local Climate Zones (LCZ) maps and assess their correlation with urban thermal comfort, while eliciting and addressing user community needs through the parallel development of open-source software tools. LCZ maps are produced and tested over the Metropolitan City of Milan by using multispectral Sentinel-2 and hyperspectral PRISMA satellite imagery, multi-source geodata (e.g. Copernicus Land Monitoring Service Imperviousness Density) and the Open Data Cube (ODC) technology.

The GEORES project with University of Bari and the Institute for Electromagnetic Sensing of the Environment (IREA) of the National Research Council of Italy (CNR) is instead developing a geospatial application meant to improve environmental sustainability and resilience to climate changes in urban areas, through a multi-risk platform composed of four main modules: (1) Sediment Connectivity; (2) Land Displacement; (3) Urban Floods; (4) Urban Wildfires. For each module, EO data (including the Sentinels, COSMO-SkyMed, SAOCOM and PRISMA), calculation models and algorithms (e.g. including interferometric Synthetic Aperture Radar techniques) are integrated to identify “hot-spots” of urban and peri-urban territory at high risk from the point of view of land degradation caused by phenomena of hydrogeological instability, sediment flow or vegetation fires. The extracted information is expressed with specific indicators (“geo-analytics”) calculated dynamically and automatically. The demonstration is undertaken in the Metropolitan City of Bari and the urban settlements in Gargano Promontory, Apulia Region, southern Italy.

In outlining the technological novelty of the algorithms and functionalities of the platforms and plugins, the paper illustrates the user-driven approach, the analysis of the user requirements, and how the novel products are outlining real perspectives for implementation by the users.

As we live in an era of big data acquisition - satellite, in-situ, wearables -, climate change and environmental risks have become much easier to map. On the other hand, domain knowledge is usually supplied by the academic sector, offering novel methodologies for hazard mapping and predictions, albeit being hard to translate those scientific-driven findings for the public administration, and society at large. Hence, public policies and public domain knowledge, including the implementation and monitoring of regulatory frameworks, often lag behind to the state-of-the-art.

As such, citizens are often ‘in the dark’ about the environmental or climatic risks surrounding them, even though about 40% of the world’s population lives within 100km of the coast, subject to sea level rise, or exposed to other weather and climate extremes such as heatwaves and droughts. Furthermore, the pressure for further urbanization and the efforts to preserve its rich natural capital are often at odds.

AtlanticSENSE builds upon these notions to leverage the state-of-the-art scientific knowledge on data acquisition, machine learning (ML) and metocean predictions to address the key environmental and climatic challenges we face, particularly in coastal settings, to deliver an user-friendly added-value information tool which content can be easily acknowledged by the society. The concept is to deploy efficient semi-automatic data science workflows on top of large arrays of freely available geospatial products (e.g., remote sensing, in-situ citizen/voluntary networks, and numerical modelling) to feed a live platform with real-time natural hazards and risks information, readily available to the community.

A preliminary proof-of-concept of the AtlanticSENSE concept has been deployed in the Greater Lisbon Area, integrating several modules already operational focusing on AIR (air temperatures, heatwaves and air quality predictions), OCEAN (ocean physics predictions, sea level rise and seawater temperature extremes) and LAND (coastal erosion, land use/land cover monitoring) domains, while the CoLAB +ATLANTIC is now seeking its validation by local stakeholders and community uptake. Further developments shall include additional layers of information, the continuous improvement of the user experience interface, and the scalability of the project to other regions.

Previous research has demonstrated the potential of combining satellite images with foundation models to predict environmental exposures, such as air pollution, and social inequalities, like house prices. Building upon these findings, we investigate the use of Sentinel-2 satellite imagery and foundation models to predict spatial signatures, which characterise urban form and function¹. We compare two machine learning approaches: a two-stage approach that does not require fine-tuning, where embeddings are first extracted from a large remote sensing foundation model and then used as input features for a separate prediction model, and a standard fine-tuned model directly predicting spatial signatures. By comparing the predictive performance of these approaches, we assess the value of fine-tuning foundation models for capturing spatial patterns related to urban form and function. Initial findings suggest that both approaches show promise. This study highlights the feasibility of combining Sentinel-2 imagery with foundation models to understand urban environments and their spatial signatures on a large scale. The results underscore the potential of this approach to provide valuable insights into urban form and function, complementing traditional data sources and offering a new perspective for urban research and policy-making.

¹ Fleischmann, M., & Arribas-Bel, D. (2022). Geographical characterisation of British urban form and function using the spatial signatures framework. Scientific Data, 9(1), 546. https://doi.org/10.1038/s41597-022-01640-8

Effective management of natural hazards and risks in urban context is essential for ensuring the safety and resilience of urban populations. The convergence of Earth Observation (EO) technologies with advanced algorithms and solutions offers unprecedented opportunities for proactive mitigation and response strategies. This paper presents the collective experiences and exemplary applications e-GEOS derived from several R&D and innovation projects focusing on different Italian cities, monitored using EO-based technology for a variety of applications.

All the projects have been instrumental in harnessing EO-based solutions to address natural hazards and risks in Italian urban areas, characterized often by ancient city centres, prone to stability issues, and suffering of temperature increase in summer season, with dangerous impact on the aged population.

Leveraging Synthetic Aperture Radar (SAR) imagery, thermal satellite data, drone-based Lidar acquisitions, and the generation of 3D city models trough MVS technique, these projects, and following activities, have pioneered innovative approaches towards the development of an Urban Digital Twin—an integrated platform for comprehensive urban monitoring and management. Through the utilization of SAR imagery, the projects have enabled the detection and monitoring of ground and structure deformation, facilitating early warning systems for geological hazards such as landslides and subsidence. Drone-based Lidar acquisitions have provided high-resolution elevation data, enhancing structural assessments and urban planning efforts. Furthermore, the generation of 3D city models has enabled the visualization and simulation of various hazard scenarios, supporting decision-making processes and community engagement initiatives. The exploitation of thermal data allowed also the definition and practical retrieval of UHI, for the benefit of citizens health. By synthesizing the experiences and lessons learned from the multi-level monitoring of Roma, Gubbio, L’Aquila, Milano, this paper underscores the transformative potential of EO-based solutions in mitigating natural hazards and reducing risks in urban environments. The development of an Urban Digital Twin represents a paradigm shift towards holistic and proactive urban management, paving the way for resilient and sustainable cities in Italy and beyond.

Adapting cities to climate change is a key challenge for humanity. Within this context, urban overheating more recently has become a high priority for cites worldwide, which are about to invest billions of Euros in measures to reduce this problem. To design sustainable urban planning strategies, detailed knowledge on the heterogeneous urban environment is urgently required. Remote Sensing in principle is a powerful means to acquire spatially explicit data for any city and thus to inform these actions and monitor their impact. However, the strong urge to use existing datasets for timely urban heat exposure monitoring and climate adaptation information must carefully consider the physical limitations of remotely sensed LST so as to not result in incomplete, wrong, or misleading indicators. Most importantly, remotely sensed LST provides a biased representation of the urban surface temperature and does not directly provide near-surface air temperature.

These pitfalls are mainly linked to the complex natures of the LST to air temperature coupling, which result in temporal and spatial mismatch of the urban surface and air temperatures respectively. Moreover, the urban LST signal requires very careful processing and interpretation due to effects of strong thermal anisotropy of heterogeneous 3D urban landscapes, emissivity assumptions, and geometrical bias towards horizontal surfaces amongst others. Readily available LST maps are therefore rather detached from the heat exposure at street level and should hence not be used directly for the support of urban planning without careful interpretation and detailed knowledge.

In this contribution we highlight the most relevant misconceptions and pitfalls with the aims to first avoid large investments based on the wrong metrics, and second to start a conversation between Urban Climate Science and Urban Remote Sensing to develop pathways towards more suitable parameters and methods that can be applied to monitor urban climate more appropriately.

The ambition of the LifeCoolCity project is to provide tools that support the management of blue-green infrastructure (BGI) in 10,000 European Union cities, aiming to strengthen their adaptive capacity to the effects of anthropogenic climate change. The project utilizes advanced technology, combining satellite imagery with high-resolution aerial data obtained through laser scanning, thermal sensors, and hyperspectral sensors. The data collected are processed using Geographic Information System (GIS) and Artificial Intelligence (AI). Moreover, our proprietary field measurements and external reference acquisition are used for data quality control and model training.

As a result, a series of analytical products to assess five factors that build the adaptive potential of cities: soil sealing, urban heat island, the quality of blue infrastructure, the quality of green infrastructure, and biodiversity level. The area of 10,000 cities is analyzed using satellite data to assess these adaptive potential factors in terms of their intensity and dynamics. Additionally, in the demonstration city of Wrocław, an intervention involving the implementation of blue-green infrastructure is being tested using the proposed decision support system.

Within the development of this system, we introduce the valuation of ecosystem services of various nature-based solutions (NBS) through consultations with residents, officials, and scientists. Next, we assess how much we can value the increase in biodiversity or the improvement of water conditions as a benefit for the residents. We propose surveys among residents with an innovative approach based on visualizations of the same place with different adopted NBS.

This allows us to consolidate the five mentioned factors into a single integrated indicator, which enables the recommendation of an appropriate nature-based solution in a given location. In cases of high natural values, the system recommends protecting the location from destruction.

As a result, city managers and residents will receive four products: CoolCity Ranking, CoolCity Report, CoolCity Design, and CoolCity Monitoring. These will help identify and enhance the adaptive needs of urbanized areas, create a strategy for managing BGI to strengthen adaptive capacities, recommend the most effective nature-based solutions, and monitor the effectiveness of their implementation.

At the conference, the system's assumptions and partial results will be presented, enabling a broad discussion on the effectiveness and applicability of the developed tools in various urban contexts. The project is co-financed by the European Union under the LIFE+ program.

The research conducts a comprehensive comparison of various European cities, emphasizing the pivotal role that nature-based solutions play in promoting sustainable urban environments. These nature-based solutions encompass a range of strategies that leverage natural processes and green infrastructure to address urban challenges. By incorporating green infrastructure, such as parks, green roofs, and urban forests, alongside ecosystem services like air and water purification, pollination, and climate regulation, cities can significantly enhance their resilience to environmental stresses.

In particular, green infrastructure helps to mitigate the adverse effects of climate change by reducing urban heat islands, managing stormwater, and sequestering carbon dioxide. This, in turn, contributes to a reduction in greenhouse gas emissions and helps cities adapt to extreme weather events. Moreover, nature-based solutions improve the overall quality of life for urban residents by providing recreational spaces, enhancing biodiversity, and fostering mental and physical well-being.

Several European cities have become exemplary models in the implementation of nature-based solutions:

Copenhagen, Denmark: Copenhagen has integrated green roofs and parks to manage stormwater and reduce flooding. The city’s Cloudburst Management Plan includes projects like the Tåsinge Plads, a multifunctional urban space that can hold excess rainwater during storms.

Vienna, Austria: Vienna has long been a leader in green urban planning. The city’s extensive green belt and urban forests are part of its strategy to enhance air quality and provide recreational areas. Projects like the Aspern Seestadt are designed to be sustainable urban districts with ample green spaces and energy-efficient buildings.

Barcelona, Spain: Barcelona has implemented a network of green corridors and parks to connect urban green spaces, promoting biodiversity and providing residents with accessible recreational areas. The city’s Green Infrastructure and Biodiversity Plan aims to increase green space per capita and enhance urban resilience.

Berlin, Germany: Berlin has successfully integrated green infrastructure into its urban landscape through initiatives like the Biotope Area Factor, which mandates a certain percentage of green space in new developments. The city’s Tempelhofer Feld, a former airport turned public park, is one of the largest urban green spaces in the world.

A crucial element in the successful implementation of these solutions is active citizen engagement. It is imperative that urban sustainability initiatives are not solely top-down but rather involve the community at every stage. When citizens are actively engaged, they are more likely to support and maintain green projects, ensuring their long-term success. Community participation can take various forms, such as public consultations, participatory planning, and citizen science projects, which empower residents to contribute to environmental monitoring and decision-making processes.

In summary, the study underscores the importance of nature-based solutions in building sustainable cities. By integrating green infrastructure and ecosystem services, European cities can not only bolster their resilience and mitigate the impacts of climate change but also enhance the quality of life for their inhabitants. The active involvement of citizens is essential in this process, ensuring that the transition to sustainable urban living is a shared endeavor driven by collective action and community spirit.

The intensity of shifting environmental conditions is expected to increase due to climate change, posing a new threat for infrastructure damage and urban resilience, resulting to infrastructure damage, commercial loss and even loss of human lives in the affected areas. A number of studies have been conducted to investigate the potential use of different Nature-Based Solutions (NBS) as a countermeasure to mitigate stormwater runoff, improving climate resilience while preserving the local biodiversity. In the current study, the impact of the traditional stone weirs on climate change-related benefit, namely storm water regulation, in more sparse populated urban areas such as the Aegean Greek islands is evaluated. The analysis for monitoring the impact of stone weirs for storm water regulation is based on a combination of space-based remote sensing observations and in-situ data, produced either from off-the-shelf sensors or citizen science participatory campaigns. Remote Sensing monitoring will also exploit very-high resolution data of Copernicus Contributing Missions analyzing different indexes, such as Normalized Difference Vegetation Index (NDVI) and ND Water Index (NDWI). Dedicated approaches and state-of-the-art models will be deployed to assess and upscale the potential of stone weirs NBS for stormwater regulation, flood mitigation and biodiversity impact at a regional scale.

Acknowledgement:

This research has been funded by European Union’s Horizon Europe research and innovation programme under CARDIMED project (Grant Agreement No. 101112731) (Climate Adaptation and Resilience Demonstrated in the MEDiterranean region).

Urban areas necessitate effective management strategies to mitigate natural risks and protect communities. By harnessing Sentinel-1 radar images, the European Ground Motion Service (EGMS), an integral component of the Copernicus program, provides comprehensive insights into ongoing ground deformation processes. The EGMS is a deferred-time, multi-purpose mapping, and – to a certain extent – monitoring tool for active ground motion in urban areas. The presentation will showcase some EGMS use cases to convince existing and new users about the added value of the product in enabling the identification of areas prone to instability and the improvement of decision-making processes.

We designed a multi-scale methodology to assess present-day and future land subsidence risk in urban areas of Italy, from the national to the local scale, with a focus on direct impacts on metropolitan landscapes (development of ground depressions, earth fissures, structural damage, increased flood risk). Ground deformation observations from multi-temporal satellite Interferometric Synthetic Aperture Radar (InSAR), hydrogeological, topographic and land use datasets are embedded into an innovative risk assessment workflow, and processed with advanced geostatistics to identify the main subsidence hotspots and drivers.

Future subsidence risk linking with the exploitation of groundwater resources is assessed by accounting for various climate change scenarios (RCP4.5/8.5, medium/high emissions), and the projected demographic and urban development in 2050 and 2100. Advanced numerical models coupling 3D transient groundwater flow and geomechanics also enable the quantification of the effects of groundwater usage to land deformation, and the estimation of uncertainties at the local scale. A bespoke socio-economic impact analysis based on the exposure, vulnerability and resilience of the affected areas, also allows the estimation of market and non-market direct/indirect losses at the national, regional and local scales.

The results for the 15 metropolitan cities of Italy, the whole Emilia Romagna region and the city of Bologna will showcase the potential of the developed methodology and its benefits to inform water resource management and decision making, towards sustainable groundwater use and urban development. This work is funded by the European Union – Next Generation EU, component M4C2: From Research to Business, investment 1.1: Fund for the National Research Programme 2021-2027 and Research Projects of Significant National Interest (PRIN), in the framework of the PRIN 2022 National Recovery and Resilience Plan (PNRR) Call, project SubRISK+ (grant id. P20222NW3E), 2023-2025.

South-East Asian secondary cities are challenged by increasing floods and droughts occurrence, while decision makers are limited in capacities and data availability to foster sustainable development. Demand assessment in Laos, Cambodian and Indonesia, led to the development of a Water risk mapping methodology that visualizes Climate change impacts and evaluates blue-green adaptation measures. We first use the CORDEX dataset to assess the regional trends and the impact of climate change on precipitation and temperature, by considering Representative Concentration Pathways (RCPs), specifically the RCP 4.5 and RCP 8.5 as future climate scenarios for the period 2020-2050. This analysis is applied on three small watersheds: Prek Te in Cambodia, Boyong in Indonesia, and Nam Xam in Laos. Thereafter, we assess the impact of climate change of streamflow, by simulating the future runoff behavior using the GR2M hydrological models for the medium and extreme RCPs and for the same timeframe, over the Boyong watershed. The utilization of future climatic predictions supports hazard assessment, together with an improved Digital Elevation Model (FABDEM+) and inundation mapping (Sentinel-1) to identify flood prone area, while drought indicators and precipitation trend analysis point out regions susceptible to extreme events. Moreover, the location of buildings and agriculture land through open-source datasets in combination with associated values aid the identification of exposed areas. Finally, information on critical infrastructure, coupled with UAV images help to evaluate degrees of adaptation to define the urban resilience to climate change induced vulnerability. Within the process appropriate weights are applied and different future alternatives are displayed. This study provides information in data-scarce regions, on the impact of climate change over the hydrological cycle, and repercussions on water availability or extreme events occurrence downstream. Therefore, valuable support is provided to urban planning and Water-Sensitive-Urban-Design to evaluate suitable adaptation options and mitigation strategies on the urban level.

The growing trend of urbanization worldwide necessitates a comprehensive understanding of geological phenomena that pose a threat to these environments. Urbanization not only transforms landscapes but also introduces complex geotechnical challenges, heightening the risk of ground instability-related hazards. The utilization of satellite Interferometric Synthetic Aperture Radar (InSAR) facilitate the identification of ground movements on a millimetric scale, providing a critical foundation for evaluating risks such as landslides, which pose a prevalent threat in hilly or steep terrain regions, sinkholes that cause unexpected damage, and subsidence resulting to structural damage.

Addressing these concerns, our study introduces an innovative, data-driven multi-risk model tailored for urban assets, integrating multi-frequency multi-temporal satellite InSAR data, multi-hazard mapping, and the physical attributes of the built environment. This work offers a dynamic, semi-quantitative framework for assessing multiple georisks, thereby facilitating mitigation measures. The model ensures the assessment of diverse geohazards over large geographic scales and high-resolution mapping units, delivering scalable and easily comprehensible results. It computes single- and multi-risk scores through the appraisal of hazard probabilities, potential damages, and building displacement rates, thereby assisting in the prioritization of urban assets for targeted intervention, with the goal of enhancing urban resilience and diminishing future economic losses.

A case study conducted in Rome, Italy, proves the model's efficacy. Assessing multi-risk for approximately 90,000 buildings, it was determined that 60% are exposed to ground instability hazards. Specifically, 33%, 22%, and 5% of these buildings mainly face sinkholes, landslides, and subsidence risk, respectively. Notably, our analysis reveals a positive correlation between historical mitigation expenses and the multi-risk scores of nearby buildings, highlighting the model's value in urban planning and risk management.

This research accentuates the potential of Earth Observation data, such as InSAR, in the domain of multi-hazard risk assessment, providing insights for informed decision-making in predictive maintenance and hazard mitigation.

Flash floods caused by extreme rainfall events pose threats of extensive damage to the urban infrastructure. Conventional methods for flood inundation modeling can be computationally expensive and time-consuming. To speed up the calculations, we develop deep learning-based flood emulators for fast, real-time estimation of rainfall-induced flood depths in urban areas.

Our deep learning models follow a modular methodology. The head of the models consists of a terrain feature encoder and a rainfall distribution encoder. These encoders are connected to a joint decoder through a fusion model. From the fused latent encodings, the decoder learns to predict the pixel-wise maximum flood water depths in the study area during the given rainfall scenario. In this work, several types of deep learning architectures such as convolutional neural networks, recurrent neural networks, and neural operators were considered for the encoder and decoder modules. Different fusion strategies were also investigated for the fusion module. Using the elevation data, additional terrain features that could influence flooding were extracted and their impact on the performance of our emulators was studied.

Compared to previous works in deep learning-based flood prediction, our proposed methodology has been trained on a wide range of urban domains and rainfall scenarios. The reference dataset for supervised learning was built using open-source elevation data with spatial resolution of 1 m of more than 300 cities in the state of North Rhine Westphalia, Germany. Using several rainfall scenarios over the study areas, the corresponding flood water depth labels were computed using the flood inundation modeling tool, FiP [https://doi.org/10.2312/pgv.20221063]. We proved the generalizability of our flood emulators by evaluating their performance on previously unseen domains, in terms of hydrological error metrics. A speed-up in inference of 10,000-fold was achieved using our deep learning-based flood emulators compared to the conventional FiP tool.

This study presents a novel methodology to enhance the sustainability of urban infrastructure, such as urban buildings, cultural heritage, bridges, roads, and railways. using the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) data from Sentinel-1 satellites during 2018–2022 in Ravenna, Italy. The research emphasizes sustainability, which represents the integrated nature of human activities and therefore the need for coordinated planning among different sectors, groups, and jurisdictions. Since the 17 Sustainable Development Goals (SDGs) were established in 2015, scientists have attempted to utilize them for sustainability assessments. Ravenna in Italy, chosen as the case study, renowned for its historical significance and cultural heritage, confronts ongoing challenges in maintaining infrastructure integrity amidst factors such as subsidence and ground instability. By considering and scaling different parameters such as safety, economy, environment, education, health, transportation, recreation, population density, and public utility alongside geological understanding, the research outlines a methodology for assessing urban sustainability and resilience, with a specific focus on subsidence risk. Initial steps involve analyzing time-series deformation patterns to highlight areas experiencing subsidence processes in Ravenna. The observed average line-of-sight (LOS) velocity is about -5 mm/year, with ground deformation near 50 mm. Deformation rates were combined with hazard and potential damage data to obtain a comprehensive risk assessment. Susceptibility of subsidence throughout the study area was employed as the hazard layer, while potential damage estimates were derived from buildings’ vulnerability and real-estate market values retrieved from census parcels and from the Osservatorio Mercato Immobiliare (OMI) database, respectively.

Integrating subsidence risk and weighted factors through geospatial analysis and modeling, this approach ranks municipalities based on their sustainability. It empowers informed decision-making, promoting the sustainability and resilience of the city's infrastructure.

Keywords: Urban Infrastructure Sustainability, PS-InSAR, Subsidence, Ground Instability, Geospatial Analysis, Modeling and Monitoring

The European Space Agency (ESA) has established the Third Party Missions (TPM) Programme to enhance the accessibility and utilization of satellite data from non-ESA missions. This initiative allows ESA to distribute and support data from a variety of missions operated by international and commercial partners, including NewSpace companies, providing scientists and researchers with additional resources for a broad range of Earth Observation applications.

In this presentation, an in-depth overview of the TPM Programme will be provided, highlighting its objectives, benefits, and the diverse range of missions included under its umbrella. We will explore how the programme facilitates the integration of complementary datasets, thereby enriching the scientific community's ability to monitor and analyse global environmental and climatic changes, among others.

Aligned with the objectives of URBIS24, this presentation will focus on how TPM data supports urban monitoring and innovative EO-integrated solutions to address urban challenges. By leveraging these datasets, urban policymakers, researchers, and service providers can gain valuable insights into urban planning, climate resilience, infrastructure management, and more, fostering new collaborations and advancements in urban Earth Observation.

SatVu, a start-up company based in London (UK) is developing a constellation of ten satellites (“HotSats”) flying a Medium Wave Infrared camera, with the mission to capture thermal information of any target of the Earth during both night and day. Our MWIR sensor offers a unique perspective of the world, imaging at an unprecedented resolution of 3.5 m, and is a significant improvement over any commercially available space-based thermal sensor.

One area where high resolution thermal data is a game-changing technology is the example is the Urban Heat Island Effect (UHI). The UN expects 68% of people to live in urban areas by 2050, up from 55% today. As global temperatures are also rising, many more people will be vulnerable to heat-related illness in the coming years. This is especially true where rapid urbanisation coincides with high vulnerability to climate change.

Every year, innovative solutions are adopted by city officials. The diversity of climate adaptation plans highlights that a strategy effective in one location may not be easily applicable to another. Each city is unique, influenced not only by its geographical location but also by factors such as demographics, land structure, and other distinctive characteristics. Regardless of that, more granular and frequent measurements of heat distribution in urban areas should become a tool in assessing the cost, effectiveness, and urgency of the local authorities’ response to extreme heat events.

In this presentation, we will showcase some of the images captured by HotSat-1 launched in June 2023. We will present the outcome of our partnership with Office of Planetary Observations who used one of our earliest data products as part of their nature-based solutions service, allowing better planning and management of cities across the world’s urban greening assets.

Challenge

The relevance of thermal remote sensing satellite data has become increasingly recognised for urban environmental monitoring in recent years, especially for tackling the growing Urban Heat Island effect. The problem with current thermal satellite data is that they are either only available in very high to high temporal resolution and low spatial resolution (>1,000m) respectively from geostationary or sun-synchronous satellites, or low temporal resolution and moderate spatial resolution of 30-100m. Such data are therefore only of limited suitability when regular monitoring of small-scale environmental factors is required.

Methodology

constellr develops the HiVE (High-precision Versatile Ecosphere monitoring mission) constellation of state-of-the-art high-resolution visible, near, and thermal infrared satellites, planned for launch by the end of 2024, to monitor land surface temperature (LST). With a 4- to 1-day revisit time (1 sat 2025 to 5 sats from 2028), 30 meters native spatial resolution, and 4-band multispectral TIR capabilities from 8-12µm enabling a temperature accuracy up 1.5 K, HiVE is uniquely equipped to provide accurate and timely data urban areas.

The HiVe´s secondary optical VNIR payload provides 10 spectral bands (similar to Sentinel-2) from 400-1000nm enabling the use of super resolution techniques for thermal sharpening.

Expected results

We will present constellr HiVE mission concept, status and provide insights into our upcoming activities to ensure high data quality from mid of 2025. On top we will present the added value of HiVE data for urban and infrastructure monitoring. We will demonstrate simulated HiVE data based on airborne campaigns as well as constellr LST30 which incorporates multiple available thermal public mission data and applies the constellr proprietary LST retrieval algorithm leading to higher sharpness at a 30m spatial resolution. We apply a time series of LST30 data on Freiburg, Germany during the 2023 heatwave to understand specific localized urban heat resilience challenges.

Lead author: Airbus Dimitri - BOULZE / Special campaigns & partnerships

Co-Author: Dassault Systèmes - Frederic BOS / Senior Client Executive

Airbus and Dassault Systèmes started discussions in 2020 about the possibility to combine Airbus expertise in 3D models production from satellite high resolution imagery; and Dassault Systèmes capabilities in physical simulation to generate “Virtual Twins”, trustworthy 3D replica of any area of interest worldwide, and use them as playground to simulate various physical phenomena. Airbus and Dassault Systèmes namely worked on Virtual Twins over various urban areas to demonstrate use cases around predictive maintenance, monitoring, prevention and crisis management.

The Rationale of Airbus and Dassault Systèmes to push for an ambition on virtual twins, namely over cities, encompasses the following assumptions:

• The number of EO sensors has soared in the last 10 years, with a resulting surge in data volume as well as data type available to EO users. With Pléiades Neo today and soon with CO3D and its native stereo acquisition, Airbus is therefore securing access to a high quality and reliable source of data for 3D content that can feed new usages.
• 3D datasets are the new frontier, and we can expect that they gradually replace 2D datasets in a large panel of uses, both in the commercial and institutional sector. Various GIS software providers (Google Earth, Google Maps, QGis, ArcGis, etc…) are defining new OGC standards allowing to visualise large 3D datasets in accessible, sometimes free tools. 3D is already included in Google Earth and it’s likely that the majority of these tools will follow the same trend, which will trigger a new leap in geospatial analysis. In a matter of years, Copernicus Services may need high resolution 3D coverage the same way they need 2D coverage today
• The advent of cloud computing: AI based solutions and analytics have transformed the way both defence and commercial users are consuming geospatial data, providing faster means to exploit data, automate processing and combine with other datasets to extract relevant insights. These technical bricks in which Airbus and Dassault Systèmes have started investing several years ago are a cornerstone of any strategy around virtual twins as the need for automation, big-data and in-depth analysis is mandatory.

In 2021, in the frame of the “France Relance” plan, Airbus and Dassault Systèmes agreed on a common goal and formed a partnership to generate trustworthy simulations and indicators that can help better anticipate environmental, social, economic or sanitary crises and establish adequate prevention and response plans. The project covered three main objectives:

• See how 3D models can be integrated in Dassault Systèmes’ simulation software environment. The exploration of use cases was spread across a wide spectrum of physical phenomena: fluid dynamics (hydro or aero), acoustic, thermal or electromagnetic propagation. The list of these physical domains is not exhaustive. A matrix of about forty use-cases/verticals/simulation services has been established and to date less than a dozen have been explored. The potential remains very important.
• Test and use different LODs (Level of Details) of 3D models and run simulations over urban areas, where the complexity of the 3D environment is the highest, and the need is the strongest. More than 10 urban areas have been produced already, using Pléiades and Pléiades Neo sensors, to calibrate the relevance and specific added value to different use cases. Several production chains were also used to test various levels of details and editing, and compare how simulations are affected.
• Analyse market size and select use cases with higher score between technical challenge and business potential. The idea being to address real use cases, on-boarding end-users with prototypes as early as possible to confirm added value of the solution, while building and packaging a service offer.

The project is still ongoing, and close to 10 additional 3D models will be produced by the end of the project, making them available for a wide variety of simulations.

End-users have been on-boarded all along the project (primarily cities and institutional actors) making sure to drive the approach and help assessing the right technical trade-offs.

The COSMO-SkyMed (CSK) system is an operational constellation of 5 SAR satellites (3 of first generation + 2 of second generation) with very high performing capabilities: very high resolution, large swaths, unique radiometric accuracy, high geolocation accuracy, better than 12 hours revisit, and several other characteristics.

Among the several COSMO-SkyMed capabilities, one of the most important is the possibility to perform interferometric acquisitions using Spotlight and Stripmap imaging modes. Especially important is the possibility to perform interferometry between Stripmap data acquired by the first and second generation, thus allowing to take advantage of a huge and very long-lasting archive with a projection to the future (2 new second generation satellites will follow in 2025-2026). Currently the system is able to acquire 5 interferometric acquisitions every cycle of 16 days.

During all the years, ASI has implemented (with the support of e-GEOS) a so-called background mission to populate the catalogue with useful acquisitions. For this we have focused on performing Stripmap interferometric acquisitions over several targets, including all the major world cities (having more than 200,000 inhabitants). In this way we have now a huge archive over all the most important urban areas that has monitored the situation for more than 10 years.

Using these acquisitions, it is possible to generate very precise change detection maps (using multitemporal combinations, with coherence analysis) and to analyze also millimetric vertical ground movements using the PS technique. Such activities allow to monitor closely the urban areas in time, regardless of the cloud cover, taking advantage of the very high resolution of the Stripmap mode (3 m) which is complementing the Sentinel-1 capabilities providing a more detailed analysis over dense urban areas.

Prométhée Earth Intelligence is a French start-up founded in 2020 aiming to democratize the use of satellite imagery to offer high value-added services in environmental or strategic intelligence. It relies on proprietary data from its own satellites, with ProtoMéthée currently in orbit, followed in 2025 by the Japetus demonstrator. By 2027, the Japetus constellation of 20 nanosatellites will be fully operational to provide high-revisit and high-reactivity data acquisition capabilities. The data will be available through the proprietary Earth Observation Platform (EOP), which enhances raw data by merging it with other complementary information sources to build solutions for operational services. This agile platform acts as a unique and aggregative access point which is linked to a cloud-based digital processing system. It is designed to facilitate EO access to a broader user community.

In particular, Prométhée Earth Intelligence offers an operational solution to protect cities from flooding, splitted in three application levels, by correlating physical and human geography :

• Situational Awareness Service to Understand, delivers a flood vulnerability map based on topographic (morphology of the relief) and contextual (land use and climate) information.
• Early Warning System to Prevent and Anticipate, highlights all the critical infrastructures and locates the potential landslide zones, evaluating the most endangered areas in case of flooding.
• Rapid Response service to Act, delivers a flood map emphasises impacted buildings and transport network status. A daily refresh will be computed on it, allowing accurate monitoring.

This new capability solution delivers a decision support capacity helpful for the authorities to handle flood events in urban areas. It is a scalable tool that complements and interfaces with existing information systems, based on the informational power of GEOINT and adaptable to current and future operational needs. This will be detailed through a scientific poster.

Urbanization is a complex phenomenon characterized by rapid changes, especially in developing countries. Despite the availability of various raster layers outlining settlements globally and large-scale building footprint databases (e.g., from Google and Microsoft), these resources suffer from infrequent updates, which quickly renders them obsolete in fast-growing regions, especially in Africa and Asia. This inadequacy is particularly acute where timely data is essential for responsive urban planning and disaster management.

To overcome this drawback, DLR’s novel “World Settlement Footprint (WSF) tracker” monitors for the first time the global settlement extent at 10m resolution every six months from July 2016 to present. Scheduled to be released open and free in 2024 and systematically updated twice per year onwards, the WSF tracker marks a significant step forward in the field of urban geography, offering profound implications for sustainable development across the globe.

The layer is underpinned by a sophisticated methodology enhanced from that used to generate the WSF2019. In particular, the implemented approach integrates temporal statistics for different indices derived from both Sentinel-1 and Sentinel-2 and employs a Random Forest algorithm for classification, all processed on the Google Earth Engine platform.

The robustness and high accuracy of the WSF tracker is being validated through comprehensive qualitative and quantitative assessments, confirming its effectiveness as a reliable source for effectively managing urban development and mitigating environmental impacts. Additionally, the layer is expected to serve as a foundation for related datasets, namely the WSF Imperviousness (estimating the percent of impervious surface area), and the WSF Population (estimating resident population density). Both of these will similarly benefit from updates every six months, thus further enhancing their relevance and utility in several applications.

The Global Human Settlement Layer (GHSL) provides global, spatially detailed, multi-temporal, regularly updated, and multi-thematic information on the distribution and characteristics of human settlements worldwide. The evolution of GHSL followed a double path. On the one hand it has sustained scientific evolution delivering continuously benchmark setting products and before other proucts; on the other it has established know-how and partnership for transforming data into information and knowledge for policy support and capacity building.

The GHSL enables multi-thematic human settlement analytics at global scale looking back with historical time series from 1975 to 2020 based on multi-sensor data from Landsat and Sentinel-2. The operational product, the Copernicus GHSL (or Exposure Mapping component) of the Copernicus Emergency management Service) allows monitoring of human settlements worldwide. It ensures continuous updates of the built-up surface fraction layer for 2022, 2024, 2026 based on Sentinel-2 data, and it is quality controlled and validated. This allows updating population grids, the settlement classification by Degree of Urbanisation and indicators such as land consumption. The projection products cover short range (i.e. 2025 and 2030), but also longer time series at decadal interval until 2100 by downscaling Shared Socioeconomic Pathway scenarios at 1 km².

Regarding the transformation of data into policy support and capacity building, GHSL teamed up with UN and international stakeholders to develop the Degree of Urbanisation, which was endorsed by the UN Statistical Commission to delineate cities, urban and rural areas for international comparison (e.g. for SDG reporting). This led to a substantial capacity building effort for national statistical offices and the generation of downstream products like the urban centre database. In parallel to the capacity building, and the uptake of the baseline data into scientific literature by many users worldwide, the GHSL has established policy support activities to ensure data support transformative policies and reporting.

Earth observation data gained from the Sentinel-1 and Sentinel-2 satellites play a pivotal role in understanding the changing patterns and dynamics of urban landscapes and environments. Multi-user requirements on European and Global scales demand observing diverse variables like soil sealing, building densities, building usage, and typology. Impervious surfaces, such as roads, buildings, and pavements, significantly impact urban ecosystems but provide indispensable information about the location, size, and dynamic of human settlements relevant for emergency services.
We address these requirements with a versatile and Copernicus-production-proven framework using state-of-the-art deep-learning-based image analysis techniques to leverage the full potential of Sentinel-1 and Sentinel-2 time series data, combining highly efficient and noise-reducing time-series parameterization methodologies with a modified deep convolutional neural network called U-Net. With this setup, we simultaneously address categorical (e.g., land cover) and continuous variables (e.g., degree of soil sealing or building densities). The respective methodologies have been implemented in a flexible yet fault-tolerant processing environment that allows for multi-cloud application scenarios and roll out to large-scale processing. The methodology is currently applied at the European scale to update the CLMS Pan-European High-Resolution Layer on the Non-Vegetated Land Cover Component and for the global-scale production of the newest Global Human Settlement Layer update. Therefore, OSM data and Microsoft Building footprints are rasterized and combined with the Sentinel-2-based time-series data to train the model in predicting building and sealing degree densities. We will give an overview of our framework and present the latest results from both (CLMS and GHSL) productions, discuss and highlight the impact of principle design decisions, and highlight the main differences compared to previous product versions.

Sustainable urbanisation is a challenge and an opportunity at the same time globally. Nowadays, there is an impressive EO capacity available to deliver quality, spatial-temporarily rich, harmonized and fit-for-purpose data capable to provide insight into essential facts on the status and long-term development of the built environment on global level. Nevertheless, a tedious process to create, handle and explore huge global data on built up areas, to define analytical units and to generate standard indicators often complicate or even prevent full adoption of this EO potential for operational activities.

Presented cooperation exploits the existing technologies developed in various ESA projects (Urban TEP, EDC) together with a new generation of global urban datasets available (World Settlement Footprint suite), to address above obstacles and streamline the production of high-quality urban information on SDG indicator 11.3.1 across all world regions in an easy interactive way. Both administrative and functional urban area analytical units are supported by the App with a goal to demonstrate the tangible support of national and local city authorities in developing countries to embrace EO technology in their work, to effectively monitor and analyse their urbanisation processes and to report on the SDG indicator 11.3.1 on sustainable urbanization.

Granted by ESA Earthnet funding the cooperation aims to support collaboration with UN Habitat on Sustainable Development (SDG) Goal 11 and the New Urban Agenda support.

Cities are generally warmer than their surroundings. This phenomenon is known as the Urban Heat Island (UHI) and is one of the clearest examples of human-induced climate modification. UHIs increase the cooling energy demand, aggravate the feeling of thermal discomfort, and influence air quality. As such, they impact the health and welfare of the urban population and increase the carbon footprint of cities. The relative warmth of the urban atmosphere, surface, and substrate leads to four distinct UHI types that are governed by a different mix of physical processes. These four types are the canopy layer, boundary layer, surface, and subsurface UHI. Surface UHIs (SUHI) result from modifications of the surface energy balance at urban facets, canyons, and neighborhoods. They exhibit complex spatial and temporal patterns that are strongly related to land cover and are usually estimated from remotely-sensed Land Surface Temperature (LST) data. In the context of ESA’ Climate Change Initiative LST project (LST_cci) we investigate how the LST of cities has changed over the last ~20 years (2002-2019) using nighttime data from Aqua MODIS. We focus on nighttime conditions when the agreement between the LST and the near-surface air temperature over cities is strongest. Our results reveal a consistent warming trend across all cities, that is on average (± SD) equal to 0.06 ± 0.02 K/year. Cities located in continental climates exhibit the most pronounced warming, of about 0.08 K/year, while those in tropical climates the least (~0.04 K /year). Our results also suggest that the cities in the Northern Hemisphere warm faster than cities in the Southern and that the cities with the strongest increase in nighttime LST are all concentrated in Middle East, where we estimated trends as high as 0.15 K/year (Doha, Qatar).

There is a significant disparity in access to electricity between the Global North and South. For example, in low-income countries, fewer than 50% of people have access to electricity. Moreover, many regions still face challenges in maintaining stable electricity access. In urban areas, spatial inequalities are pronounced; poorer neighbourhoods often lack or have only informal connections to the electrical grid (or off-grid access), leading to a noticeable absence of streetlights. The Sustainable Development Goals, especially SDG 7, stress the importance of universal access to reliable electricity. In poorer neighbourhoods where Artificial Light At Night (ALAN) is available, it is typically unshielded, resulting in inadequate lighting for nighttime outdoor activities and significant light pollution. To effectively contribute to the societal debate on the need for sustainable ALAN and to minimize unnecessary light pollution, access to high-resolution nighttime remote sensing data is crucial. However, our current understanding of ALAN largely depends on datasets from a limited number of remote sensing missions, which typically have sensors with low spatial and spectral resolution. Alternative, high-resolution sources such as images from the International Space Station (ISS) and new data sources like SDGSat-1 are not yet widely used. These novel and alternative sources provide improved spectral and spatial resolution. Our study provides insights into the potential for global monitoring of ALAN using high-resolution nighttime light remote sensing imagery. We also identify user requirements for upcoming satellite-based sensors in the context of a European Space Agency-funded research project, NightWatch

Urban surface fabric identification and mapping present a significant challenge in the field of Earth Observation. Precise knowledge of surface characteristics is essential for effective urban planning and climate research. However, the presence of artificial materials in urban areas and mixed pixels complicates the accurate analysis of spectral data from multispectral and hyperspectral sensors. In turn, the current hyperspectral libraries, which rely on spectroradiometers, do not offer enough spectral variability from artificial materials for successful Machine Learning model training and accurate material detection. To address these gaps, the urbisphere urban hyperspectral library is currently being developed, utilizing the Spectral Evolution RS-3500 spectroradiometer and HySpex VS-620 Camera.

Today, the urbisphere urban hyperspectral library contains more than 5000 in-situ hyperspectral measurements from various natural and artificial materials collected from several European cities (e.g., Heraklion, Paris, and Berlin) and is planned to be enriched further in the coming years. The library also contains the respective adjusted spectra for several satellite sensors (e.g., Sentinel-2, Landsat-8, Planet SuperDove, EnMap, PRISMA, etc.), enabling satellite image classification without the need for time-consuming on-site data collection.

In this study, the current performance of the urbisphere urban hyperspectral library was tested over the broad urban area of Heraklion city. Specifically, the X-SVM classifier was trained using only the adjusted satellite spectra from the library for the satellites of Planet SuperDove, Sentinel-2, Landsat-8, EnMap, and PRISMA, while the trained models were applied to the respective satellite images acquired between August 3rd and 5th, 2023. The results highlight the current performance of the library for satellite image classification and the unique limitations that originate from the low spatial resolution for the Hyperspectral satellites (EnMap, PRISMA) and, on the other hand, from the low spectral resolution from the multispectral sensors (Sentinel-2, Planet SuperDove).

Urban planning and city governance require innovative solutions to face new urgent requirements and priorities. Leveraging advancements and the integration of Earth Observation (EO) with Artificial Intelligence (AI) methodologies has become critical in urban management.

Using AI models to chain multiple processes in a pipeline, from Super Resolution (SR) to Change Detection (CD) and Building Footprints (BF) extraction, is crucial for urban delineation, providing stakeholders with accurate results for informed decision-making. The project IRIX4US aims to monitor urban dynamics accurately for mobility, sustainability, urban planning, and accessibility in urban areas.

Engaging a broad set of relevant users and stakeholders, from public organizations to private industry, provided an opportunity to develop an EO-integrated solution tailored to the needs of urban experts and decision-makers. The solution consisted in a comprehensive AI pipeline:

1. Fine-Tuned SR of Sentinel-2 (S2) images: An AI-driven solution has been conducted to enhance their spatial resolution up to 2.5 meters. The model was trained using data from very high-resolution satellite imagery at a European scale. This process outputs a realistic representation of buildings, ensuring sharp edges and clear differentiation from surrounding objects such as roads.
2. Segmentation and BF extraction: IRIX4US adapts tested segmentation models to the super-resolved S2 imagery in urban landscapes, extracting visible and near-infrared (VNIR) bands and BFs using high-quality labels provided by champion users.
3. CD with AI Models: AI models have been employed for identifying differences in BFs between different images over time. These AI models were trained using temporal change data provided by champion users, allowing for the effective monitoring of urban development patterns in a multi-temporal context.

The results within the IRIX4US provides a dynamic and time-saving advancement for urban planning, proven feasible and scalable applications such as building segmentation, building change detection, illegal settlement identification and construction damage assessment.

This study investigates the efficacy of image restoration techniques of satellite EO images with a focus on super-resolution of Sentinel-2 and PlanetScope products. The ultimate goal is to develop a robust image restoration model capable of producing enhanced multispectral aerial-like imagery. The study is investigating several semi-supervised generative algorithms including SR-GANs, EDSR-GANs, WDSR-GANs, Lambda-PNN and W-Net.

The neural network architectures examined exhibit variations in their learning approaches and the potential utilization of a HR panchromatic component. For instance, the SR-GANs, EDSR-GANs, WDSR-GANs and WNet architectures are trained within a semi-supervised framework, i.e. supervising the training process of the generative models by incorporating multispectral HR target images. The Lambda-PNN network is trained within a fully unsupervised framework, hence no HR target images are adopted during its training phase. On the other hand, Lambda-PNN and WNet include a HR resolution panchromatic channel aiding the image super-resolution task. For our assessment with Lambda-PNN and WNet architectures, we opted for a panchromatic channel from an aerial image captured at a spatial resolution of 75cm either within the same date and in year 2019.

From our preliminary experiments, we have observed that GAN architectures which do not require a high-resolution panchromatic band can reconstruct HR scenes only for synthetic LR image datasets at 3m resolution obtained by downscaling aerial images. Pan-sharpening architectures like Lambda-PNN do not require a multispectral ground truth for training. However, when fed with Sentinel-2 and PlanetScope images, such architecture can produce synthetic images whose spatial structure is preserved (low structural loss) but yields unrealistic results regarding spectral information.

Among the preliminarily investigated architectures, the only architecture yielding consistent and plausible predictions is W-NET, a GAN fed with panchromatic and LR images and trained using HR target images employing a supervised approach. Furthermore, we are dedicating our effort in quantifying the reliability of generated images with respect to the introduction of spatial and spectral artifacts.

A myriad of global/regional datasets have provided valuable insights into environmental dynamics at global scale, including urban environments, however they often fall short of capturing the fine-scale nuances of the state and dynamics at local levels. Most existing datasets lacks the resolution and specificity required to address the diverse monitoring needs of urban stakeholders, particularly in densely populated or rapidly changing urban landscapes.

In response to these limitations, there is a growing recognition of the need for human-in-the-loop approaches, wherein stakeholders actively engage in the process of data collection, analysis, and interpretation to augment existing datasets and tailor monitoring efforts to local contexts. In this presentation, we will present a new agile cloud-based solutions that empower users to independently create and update urban datasets using free and open Copernicus Sentinel and NASA Landsat data, thereby overcoming the challenges associated with existing global datasets and fostering a more dynamic and responsive approach to urban environmental monitoring.

Developed as part of the EU 100Ktrees activity, based on the elaborated needs of municipalities worldwide, you will hear how complex machine learning frameworks and automated satellite data acquisition has been turned into user friendly cloud-based web applications for scalable mapping of urban environments on demand. You will learn how multitemporal satellite data and automated data analysis can turn raw satellite data into scalable information about urban heat islands, green spaces, flood exposure and impervious surfaces. And you will discover how these tools, and others, are vital to address the existing data gap across urban landscapes worldwide and how they can be used to underpin comprehensive and dynamic monitoring regimes.

Urban areas are central to a range of sustainability challenges, ranging from land take and the loss of natural habitat to the exposure to climate change impacts. As a result, a large number of models have been presented that project future urban land change under different scenario conditions. The sustainability of future developments depends strongly on the characteristics of urban land, including for example the density, the building material, and the presence of slums. Yet, most of urban change models include only one type of urban land, corresponding with areas recognized as built-up land in satellite imagery. This severely limits their relevance for understanding urban development in general and for sustainability assessments specifically. Here, we present an urban growth model that includes multiple classes along the rural-urban gradient and that is applied to model urban development in five countries in Southeast Asia. In this model, change is driven by future population developments, but these can lead to both expansion and densification, depending on the scenario. In addition, we characterize the different types of urban land in terms of their building materials and demonstrate the relevance of this characterization for assessing future risk for river flooding. Interestingly, the expansion scenario leads to a lower expected annual damage in Lao PDR and Cambodia, while the densification scenario leads to a lower expected annual damage in Myanmar, Thailand, and Viet Nam. Finally we point at the opportunities that recent developments in remote sensing provide for better characterization of urban areas, and thus also for more relevant urban models.

In the context of GDA-APP (Global Development Assistance - Analytics and Processing Platform) project, a segmentation model has been developed in order to identify urban areas using Sentinel-2 composite image (NRGB). The objective is to create a fully-automatic module to delineate urban area for instance for change detection, informal settlements detection, damage infrastructure assessment, etc…

The modeling pipeline follows several training steps. The first step is corresponding to a pre-training for segmentation using BigEarthNet dataset ~500.000 patches of Sentinel-2 as input and its respective ESA Worldcover building layer as reference: in this way the model will learn how to interpret Sentinel-2 imagery with the objective of detecting urban area. The second step is to improve detection to building scale by using pairs of image of Sentinel-2 composite and Microsoft building layer corresponding mask with ~30.000 patches. The third step improves delimitation capabilities by using a similar dataset but by enhancing spatial resolution from 10m to 3m using a super resolution model. All model versions are made in order to be responsive to any type of urban landscape and performing the segmentation at a global scale; indeed selected images are located over various areas distributed worldwide to make it as much general as possible. Moreover the complexity of the task requires several training steps to go deeper in precision, in terms of spatial resolution and so in building delimitation. The overall architecture corresponds to a UNet composed of 34 layers with ResNet blocks, and optimized with the use of loss function such as focal loss, architecture and functions well known in machine learning for segmentation application. Current model is trained with 10m pairs of images (cf step 2), its performance is estimated through accuracy metric, giving an overall score of more than 95% however the real interest is find in the accuracy of buildings detection, which is approximately to 65%. By following each steps described above, pre-training and final learning with 3m spatial resolution imagery are promising for improve results.

Monitoring Sustainable Development Goal (SDG) indicator 11.3.1 on land use efficiency is crucial for sustainable urban development. This indicator, which assesses the ratio of land consumption rate to population growth rate (LCRPGR), helps urban planners and policymakers optimize land use and curb unsustainable urban sprawl. Efficient land use supports several interlinked SDGs by promoting denser urban living, reducing cities' ecological footprints, enhancing livability, and preserving natural resources. Thus, monitoring this indicator is vital for ensuring sustainable growth and improving quality of life in urban environments.

Earth observation (EO) time series offer a powerful tool for monitoring urban land use efficiency, providing key insights into the dynamics of urban expansion and development. This presentation will first introduce a state-of-the-art method for urban mapping at global scale, leveraging open EO data such as Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Imager (MSI) data and deep learning. Then it will showcase the SDG11.3.1 LCRPGR results in 500 cities during 1985 to 2020 using existing built-up datasets derived from Sentinel-1/2 and the extensive archive of Landsat long time series. The findings demonstrate that while population datasets from various sources minimally affect LCRPGR calculations, different built-up datasets lead to significant variances. Moreover, challenges arise when employing improved built-up products derived from Sentinel-2 data together with the built-up products derived from Landsat data, impacting the LCRPGR calculations. Consequently, this has led to many cities showing a negative LCRPGR during 2015-2020, inaccurately suggesting a decline in density. These results highlight the urgent need to enhance the accuracy and consistency of built-up data, given its significant role in determining the values of SDG 11.3.1 Land Use Efficiency.

The Sustainable Development Goals (SDGs) are a universal agenda to address the world’s most pressing challenges. Robust monitoring mechanisms and timely, accurate and comprehensive data are essential in guiding policies and decisions for successful implementation of the SDGs. Yet current ways of monitoring progress towards the SDGs such as through household surveys cannot address the SDG data gaps and needs. Along with EO data, citizen science offers a solution to complement traditional data sources.

The complementarity of citizen science and EO approaches for SDG monitoring has also been acknowledged in the literature. For example, the authors of this contribution carried out a systematic review of all SDG indicators and citizen science initiatives, demonstrating that citizen science data are already contributing and could contribute to the monitoring of 33 per cent of the SDG indicators. As part of this review, they also identified overlap between contributions from citizen science and EO for SDG monitoring. One specific citizen science tool integrating citizen science and EO approaches that could complement and enhance SDG monitoring is Picture Pile. Picture Pile is a web-based and mobile application for ingesting imagery from satellites, orthophotos, unmanned aerial vehicles or geotagged photographs that can then be rapidly classified by volunteers. Picture Pile has the potential to contribute to the monitoring of fifteen SDG indicators covering areas, such as deforestation, post disaster damage assessment and identification of slums, among others, which can provide reference data for the training and validation of products derived from remote sensing.

This talk presents the potential offered by Picture Pile and other citizen science tools and initiatives focusing on urban applications to complement and enhance official statistics to monitor several SDGs and targets including SDG 11 Sustainable Cities and Communities. Recommendations will also be provided for how to enable partnerships and collaborations across data communities and ecosystems in order to mainstream citizen science and EO data for SDG monitoring and reporting of urban issues.

The rapid urbanization combined with insufficient low-cost housing supply in the Global South results in the proliferation of informal settlements. Our study addresses the pressing need for accurate information by User and Data-centric Artificial Intelligence (AI)-based methods for mapping informal areas. In collaboration with local communities and several (inter)national stakeholders, we co-designed an AI-workflow based on free-cost Earth Observation (EO) and geospatial data to map informal areas in eight cities across the globe. Together with our Early Adopter and Stakeholders, we identified local data questions and validation needs. For example, in Buenos Aires, the main question is to identify unmapped settlements (compared to the national registry). Presently, we are evaluating the performance of the Multi-Branch Convolutional Neural Network (MB-CNN) model across our pilot cities. The model takes Multispectral and SAR data in combination with additional geospatial features (e.g., building density) as input and delivers the segmentation map. This is done in preparation for increasing the reference data quality via our user portal. The initial model results (for informal areas) show for the city of Nairobi an F1 score of 0.79 and for Medellin an F1 score of 0.75. Mumbai, Salvador and Buenos Aires also performed relatively well, with F1 scores of 0.70, 0.61 and 0.57. In contrast, the model performs below 0.5 in Jakarta, Lagos and Mexico City (more complex cities in terms of morphology and reference data). The results indicate that the fusion of datasets provides higher accuracy than using only S-2. This can be attributed to the additional contextual information, which aids in more accurate identification and mapping. Cities with large and densely built-up settlements (e.g., Medellin, Mumbai, Nairobi) significantly improved when building morphology was added. Next, we will be improving reference data by crowdsourcing via our portal.

Informal settlements are the primary residential areas for the urban majority. Despite their significance, a lack of accurate data and often outdated records pose significant challenges for public entities in meeting residents' needs. Consequently, our understanding of the spatial distribution, evolving patterns, and growth over time of these settlements remains limited. However, emerging remote technologies offer new opportunities for mapping and collecting high-quality data. This presentation focuses on the utilization of urban data and AI/ML-based mapping technologies to gain deeper insights into the complex realities of informal settlements. In this context, we will present UNITAC's current research and introduce our work on the Building & Establishment Automated Mapper (BEAM), specialized software for AI/ML-based detection of building footprints in informal settlements

The BEAM tool, developed by UNITAC in partnership with eThekwini Municipality in South Africa, leverages AI to map rooftops in high-resolution aerial photography. It meets a crucial need identified by the municipality for automated mapping processes, crucial in a city where a quarter of residents reside in informal settlements, and census data updates occur only every 10 years. This results in a lack of accurate data about its population, particularly in informal settlements, complicating infrastructure planning and human settlement upgrades. Additionally, the BEAM tool is being upscaled for use with satellite imagery (currently it works on high-resolution aerial photography) in eight Central American cities. Consequently, the tool underscores its innovative support for city governments in the global South, leveraging machine learning to assist these governments in addressing key sustainable development goal targets and indicators, as well as tackling the urgent challenges of urbanization.

The characterization of structural changes in urban areas, and specifically the current trend towards verticalization, is a crucial step toward understanding urbanization phenomena at the global level. The use of spaceborne remote sensing data for this task has grown over the last decade, focusing on SAR and multispectral data, often jointly used. In particular, very high resolution (VHR) data, increasingly available in large volumes, calls for efficient and accurate 2D/3D change detection techniques, able to go beyond existing methods, focused either on detecting buildings footprint changes over time or on extracting changes in the number of floors/height of the same buildings. In this work, we present a complete framework applicable to VHR SAR temporal sequences able to characterize 2D & 3D changes at the same time. The methodology involves the extraction of altered buildings in the initial phase and subsequently performs a building segmentation, with specific emphasis on discerning changes in building height. The identification of changed urban core areas is performed using an unsupervised temporal clustering applied to the original sequence of SAR amplitude and coherence images. Subsequently, building segmentation in the initial and ending image of the sequence is performed thanks to a deep learning U-Net architecture, allowing a quantitative characterization of the 2D changes. Finally, the building height extraction is accomplished through the utilization of a novel ResNet deep learning architecture. The proposed framework has been tested using a time series of three years of COSMO-SkyMed data, from January 2019 to November 2021, over the city of Milan, Italy. Experimental results in the "Gae Aulenti" and "City Life" areas show that 2D and 3D changes are correctly detected and the use of the proposed machine and deep learning framework significantly increases the ability to achieve a better characterization of structural changes in urban areas.

While the significance of built-up volume and density for future sustainability and resilience of the built environment is widely recognized, the interplay between 3D built-up patterns, economic development, and local planning policies remains relatively unexplored. To address this gap, we integrate novel global data from the World Settlement Footprint 3D (WSF® 3D) - encompassing building area, height, and volume -, with socioeconomic statistics and information on planning policies across all countries and over 12,000 urban clusters worldwide. Our analysis begins with enhancing the original WSF3D dataset to provide a more accurate representation of high-rise buildings exceeding 50 meters. This enhancement involves integrating data from the Emporis database, a leading source of tall buildings information globally. Subsequently, we merge the enhanced WSF3D data (WSF3Dv2) with socioeconomic statistics sourced from the World Development Indicators (WDI) database and data on zoning regulations and land-use policies provided by the World Bank. For the city-level analyses, we additionally utilize the Urban Center database provided by the Joint Research Center (JRC) of the European Commission. Through our study, we offer a comprehensive and spatially detailed understanding of the global 3D building stock, unveiling intricate relationships between 3D settlement morphology, economic development, and local planning regulations. This empirical evidence strengthens the understanding of the global megatrend of urbanization and provides crucial insights to enhance the effectiveness of land use and spatial development policies aimed at promoting (urban) sustainability and resilience.

Accurate building height estimation is essential for sustainable urban planning, monitoring, and environmental impact analysis. However, conducting large-scale building height estimation at a fine spatial resolution is challenging, especially using open-access satellite data. Existing large-scale solutions provide height at coarse spatial resolution (500 m - 90 m), a better resolution can provide more comprehensive understanding of urban development.

We propose an advanced deep learning model, T-SwinUNet, specifically designed for large-scale building height estimation at a fine spatial resolution of 10 m. The model harnesses salient features from the spatial, spectral, and temporal dimensions of the Sentinel-1 SAR and Sentinel-2 MSI time-series data.

In T-SwinUNet, we integrated the semantic feature learning capabilities of the CNN encoder with the local/global feature comprehension capabilities of Swin transformers. With added temporal attention, the model learns the correlation between constant features (mostly geometry) and variable features (e.g. shadow) of building objects over time. This not only aids in differentiating building from non-building objects but also condition model to learn salient building height features. We equipped T-SwinUNet with uncertainty prediction, which helps in assessing model’s robustness and transferability to new areas. Knowing uncertainty in predictions helps stakeholders to make informed and careful decisions.

The model is evaluated on data from the Netherlands, Switzerland, Estonia, and Germany. The extensive evaluation and comparison with state-of-the-art DL models show that our proposed T-SwinUNet model surpasses SOTA by achieving an RMSE of 1.89 m at 10 m spatial resolution. We conducted a detailed ablation study to understand the impact of time-series data, each modality, multi-task learning and others. Further assessment at 100 m resolution shows that our predicted building heights (0.29 m RMSE, 0.75 $R^{2}$) also outperformed the global building height product GHSL-Built-H R2023A product (0.56 m RMSE and 0.37 $R^{2}$).

Accurate determination of building heights is crucial for urban 3D development analysis and disaster risk assessment. State-of-the-art (SOTA) techniques typically treat height retrieval from buildings as a regression problem. For instance, [1, 2] propose supervised Multimodal Deep Learning (DL) frameworks to estimate building heights using Sentinel-1 (Sen-1) and Sentinel-2 (Sen-2) data.

In this work, we employ an Attention-based U-Net model for building height estimation, relying solely on radar information provided by Very High-Resolution (VHR) COSMO-SkyMed (CSK) data. We utilize a CSK Level-1D Stripmap Himage acquisition mode with a spatial resolution of 2.5 meters in Ascending orbit direction, covering the entire metropolitan area of Milan city for the model training. Ground Truth (GT) labels are derived from the Normalized Digital Surface Model (nDSM) and refined by using a binary footprint mask from OpenStreetMap (OSM) data to exclude non-built-up (BU) areas.

Our proposed model exhibits robust performances in terms of Root Mean Square Error (RMSE) on external test sites in the cities of Pavia, Sant’Angelo Lodigiano, and Lodi, with final error values of 0.781, 0.731, and 1.487 meters, respectively, outperforming previous studies [1, 2]. To further validate the model, the average error outside the buildings was also evaluated. The results reveal a general underestimation of building height as their actual height increases, indicating an avenue for future research.

Notably, our method capitalizes on CSK radar data, offering a swift solution for mapping 3D BU area features owing to its VHR, weather-independent capabilities, and rapid emergency response.

REFERENCES

[1] Yadav, R., https://arxiv.org/abs/2307.01378

[2] Bowen, C. et al., https://www.sciencedirect.com/science/article/pii/S1569843223002236

CARS is a CNES open-source 3D reconstruction software part of the Constellation Optique 3D (CO3D) mission. CARS stands out from other MultiView-Stereo methods due to its highly parallelizable design, capable of addressing large volumes of data for processing on an HPC cluster or personal machine.

Designed as a modular set of applications, CARS allows user to plug in new public or confidential contributions, such as new applications or pipelines. Using this plug-in concept, we recently addressed the need for LOD2 semantized meshes on urban areas. The new CARS plug-in makes the most out of intermediate CARS products such as uncertainty and classification layers to create LOD2 meshes.

Surface temperatures are central to the surface energy balance, and particularly in cities link to human thermal comfort and energy consumption. Since, cities have a strong vertical component, estimating the surface temperature for the complete three dimensional (3D) urban surface is important. While satellite remote sensing holds a strong potential in observing city-wide and global urban surface temperatures, these need to be complemented with in-situ infrared observations and modeling to achieve the assessment of the complete 3D surface temperature.

In this study, a sub-building scale three-dimensional (3D) energy balance model is evaluated using a novel ground-based thermal camera observatory in Berlin. Four thermal cameras (OptrisPI160) were mounted on an 80 m tall building, overlooking a mix of low-rise residential, parks, trees from all cardinal directions. 3D urban surface temperature was modeled using an extension of the energy balance model TUF-3D model (Krayenhoff and Voogt, 2007) including a vegetation component (VTUF-3D, Nice et al., 2018).) Earth Observation products (land cover, building and vegetation heights) were used to parametrize the 3D urban form of the study domains, while forcing data were available from meteorological measurements on the same building. A total area of 0.2 km² was simulated with a 5 m spatial resolution for 3 – 6 August 2022. The camera brightness temperatures observations were corrected for emissivity to conclude to surface temperature. Detailed emissivity maps were derived using land cover and reference spectral library information.

The diurnal pattern of modelled surface temperature was found similar to the observed one for different surface types (MAE ranging between 3-10 C depending on the area and time of day, with error increasing during daytime). VTUF-3D simulated surface temperatures for more areas in Berlin will be used to assess the complete temperature from satellite thermal observations, such as Sentinel-3.