the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Brief Communication: AI-driven rapid landslides mapping following the 2024 Hualien City Earthquake in Taiwan
Abstract. On April 2nd, 2024, a Mw 7.4 earthquake struck Taiwan’s eastern coast, triggering numerous landslides and severely impacting infrastructure. To create the preliminary inventory of earthquake-induced landslides in Eastern Taiwan (3,300 km2) we deployed automated landslide detection methods by combining Earth Observation (EO) data with Artificial Intelligence (AI) models. The models allowed us to identify 7,090 landslide events covering >75 km2, in about 3 hours after the acquisition of the EO imagery. This research underscores AI’s role in enhancing landslide detection for disaster response and situational awareness, and its implications for understanding earthquake-landslide interactions to improve seismic hazard mitigation.
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Status: open (until 20 Nov 2024)
Data sets
AI-driven rapid landslides mapping following the 2024 Hualien City Earthquake in Taiwan Lorenzo Nava, Alessandro Novellino, Chengyong Fang, Kushanav Bhuyan, Kathryn Leeming, Itahisa Gonzalez Alvarez, Claire Dashwood, Sophie Doward, Rahul Chahel, Emma McAllister, Sansar Raj Meena, Xuanmei Fan, Xiaochuan Tang, and Filippo Catani https://zenodo.org/records/11519683
Interactive computing environment
SAR-LRA Tool V1 for Google Colaboratory Lorenzo Nava, Alessandro Mondini, Kushanav Bhuyan, Oriol Monserrat, Alessandro Novellino, and Filippo Catani https://github.com/lorenzonava96/SAR-and-DL-for-Landslide-Rapid-Assessment/tree/main/SAR-LRA%20Tool%20V1%20for%20Google%20Colaboratory
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