Conference Agenda

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