Ebel P., El Baz M., Wang J., Xuan W., Qi H., Zheng Z., Yokoya N., Park J., Park J., Elskens A., Charles E., Modica I., Foltz Z., Bally P., Bossung C., Chini M., Longépé N., Meoni G.
IEEE Geoscience and Remote Sensing Magazine, vol. 14, n° 3, pp. 189-205, 2026
Earthquakes are a destructive and oftentimes unanticipated force of nature. To facilitate timely disaster relief, very high-resolution (VHR) spaceborne observations can map urban destruction even over remote or inaccessible terrain. Fostering community-driven innovation on artificial intelligence (AI)-based solutions for rapid mapping of building-level damage, the European Space Agency (ESA) U-Lab and the International Charter “Space and Major Disasters” jointly organized the “AI for Earthquake Response” competition. The activity was designed to emulate the needs and urges of real postevent activations. In its course, more than 261 teams participated on the ESA U-Lab Challenges platform and the best-performing AI model accomplished an overall F1 scor of 0.71. This work summarizes the competition’s objective data provided, and outcomes of the challenge. Description for each of the three best-performing AI solutions and thei workflows are provided, plus an overview summarizing thei recipes for success. We foresee the event and this report as fos tering further innovation in the community, working towar data-driven rapid mapping that may in the future suppor real postseismic activations and save human lives.
