On 2 April 2024, a M-w 7.4 earthquake struck Taiwan's eastern coast, triggering numerous landslides and severely impacting infrastructure. To create a preliminary inventory of the earthquake-induced landslides in Eastern Taiwan (3300 km(2)), we deployed automated landslide detection methods by combining Earth observation (EO) data with AI models. The models identified 7090 landslide events covering >75km(2) within approximate to 3h of the acquisition of the EO imagery. This research showcases AI's role in rapid landslide detection for disaster response. The landslide inventory generated can also be used to improve the understanding of earthquake-landslide interactions and thus improve seismic hazard mitigation.

Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan

Bhuyan, Kushanav
Data Curation
;
Meena, Sansar Raj
Data Curation
;
Catani, Filippo
Conceptualization
2025

Abstract

On 2 April 2024, a M-w 7.4 earthquake struck Taiwan's eastern coast, triggering numerous landslides and severely impacting infrastructure. To create a preliminary inventory of the earthquake-induced landslides in Eastern Taiwan (3300 km(2)), we deployed automated landslide detection methods by combining Earth observation (EO) data with AI models. The models identified 7090 landslide events covering >75km(2) within approximate to 3h of the acquisition of the EO imagery. This research showcases AI's role in rapid landslide detection for disaster response. The landslide inventory generated can also be used to improve the understanding of earthquake-landslide interactions and thus improve seismic hazard mitigation.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3560600
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