Conference Agenda

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