AI for Earthquake Response: Outcomes & insights from a global spaceborne rapid mapping challenge [* Cross-Reference *]

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Mounia El Baz; Patrick Ebel; Junjue Wang; Weihao Xuan; Heli Qi; Zhuo Zheng; Naoto Yokoya; Junghwan Park; Junghwan Park; Jaewan Park; Jaewan Park; Arthur Elskens; Éléonore Charles; Iacopo Modica; Zachary Foltz; Philippe Bally; Christian Bossung; Marco Chini; Nicolas Longépé; Gabriele Meoni, 2026, "AI for Earthquake Response: Outcomes & insights from a global spaceborne rapid mapping challenge [* Cross-Reference *]", https://lore.list.lu/citation?persistentId=perma:LIST.RSK5XM, LIST Open Repository, V1
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Description
Earthquakes are a destructive and oftentimes unanticipated force of nature. To facilitate timely disaster relief, very high resolution spaceborne observations can map urban destruction even over remote or inaccessible terrain. Fostering community-driven innovation on AI-based solutions for rapid mapping of building-level damage, ESA Φ-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 urge of real post-event activations. In its course, over 261 teams participated on the ESA Φ-lab Challenges platform.The main contribution of this work is to report the key setup and outcomes of the challenge as well as share with the community the winning strategies of the most competitive solutions. We will first provide an overview of recent and related work, then detail the core premises of the competition, including the two-phase structure of the challenge as well as its evaluation principles and data. We will also provide descriptions of the winning strategies of the best-performing teams, comprising details on data preparation, the data-driven modelling approach, and the respective team’s recap and discussion on their accomplishments. We will also review similarities or differences across models and distill key insights. Finally, we conclude by reviewing key findings and highlighting open challenges and opportunities for future contributions in rapid mapping for building damage assessment.We foresee this work to foster further innovation in the community, working towards data-driven rapid mapping that may in the future support real post-seismic activations and save human lives. (2026-03-14)
***This entry has been automatically imported via OpenAlex by LIST harvest scripts. Please refer to https://doi.org/10.5194/egusphere-egu26-18676 for the original and latest version of the publication*** (2026-07-01)
Subject Astronomy and Astrophysics; Computer and Information Science; Earth and Environmental Sciences; Engineering; Physics
Keyword Key (lock), Work (physics), Charter, Core (optical fiber), Disaster response
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