During the project, both technical and market development activities were pursued in parallel. The market and policy analysis examined existing food security indexes and EO-derived indicators at global, national, and household levels. This review identified significant methodological gaps, including the limited use of high-resolution EO data in developing scalable, data-driven food security indicators.
On the technological front, the project advanced two principal technological directions for enhancing EO–based services relevant to food security: high-resolution environmental and vegetation mapping, and large-scale urban infrastructure analysis. High-resolution mapping supports robust monitoring of cropland distribution, vegetation productivity, and ecosystem condition, key determinants of food availability and ecological sustainability. Urban infrastructure analysis complements this by characterizing settlement density, built-up extent, and expansion patterns, thereby linking food demand, accessibility, and environmental pressures. Together, these components provide an integrated geospatial basis for analysing interactions between agricultural production, ecosystem function, and human consumption dynamics.
Within environmental and vegetation mapping, the project prioritised tree species mapping due to the critical role of many species as direct or supplementary food sources, especially in tropical and rural regions. Accurate information on species distribution, range shifts, and stress signals is essential for anticipating food supply risks and for assessing ecosystem services that support agriculture. However, global tree species mapping remains limited by insufficient labelled data. To address this, the project produced GlobalGeoTree, the first comprehensive global dataset for tree species classification, containing 6.3 million geolocated occurrences spanning 275 families, 2,734 genera, and 21,001 species. A baseline vision-language model, GeoTreeCLIP, pretrained on GlobalGeoTree, showed substantial improvements in zero- and few-shot classification over existing models. The dataset, model, and code have been publicly released.
For urban infrastructure analysis, the project extended earlier work from the ERC Starting Grant So2Sat, employing weakly supervised domain adaptation to mitigate the scarcity of detailed reference data. This enabled the generation of high-resolution land cover maps for ten global cities. In parallel, the project contributed to the GlobalBuildingAtlas, the first global, high-quality dataset of individual building footprints, heights, and LoD1 3D models. Comprising 2.75 billion polygons, it surpasses all existing datasets and includes a 3 × 3 m global height layer. Although not a formal EO4FoodSecurity deliverable, its development benefited directly from methodological advances consolidated within the project.
The project also performed an urban heat island (UHI) assessment across 80 German cities using remote sensing, landscape metrics, and interpretable machine learning. Results showed strong context dependence in UHI drivers, including contrasting effects of tree-cover edge density depending on local vegetation–built-up ratios. This framework provides a transferable methodology for integrating microclimate and land-cover indicators into regional food security assessments.
