Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
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https://doi.org/10.5194/nhess-2024-146
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2024-146
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Preprints
09 Oct 2024
 | 09 Oct 2024
Status: this preprint is currently under review for the journal NHESS.

Brief Communication: 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, and Filippo Catani

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.

How to cite. Nava, L., Novellino, A., Fang, C., Bhuyan, K., Leeming, K., Alvarez, I. G., Dashwood, C., Doward, S., Chahel, R., McAllister, E., Meena, S. R., and Catani, F.: Brief Communication: AI-driven rapid landslides mapping following the 2024 Hualien City Earthquake in Taiwan, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2024-146, in review, 2024.
Received: 02 Aug 2024 – Discussion started: 09 Oct 2024
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Lorenzo Nava, Alessandro Novellino, Chengyong Fang, Kushanav Bhuyan, Kathryn Leeming, Itahisa Gonzalez Alvarez, Claire Dashwood, Sophie Doward, Rahul Chahel, Emma McAllister, Sansar Raj Meena, and Filippo Catani

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Lorenzo Nava, Alessandro Novellino, Chengyong Fang, Kushanav Bhuyan, Kathryn Leeming, Itahisa Gonzalez Alvarez, Claire Dashwood, Sophie Doward, Rahul Chahel, Emma McAllister, Sansar Raj Meena, and Filippo Catani

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

Lorenzo Nava, Alessandro Novellino, Chengyong Fang, Kushanav Bhuyan, Kathryn Leeming, Itahisa Gonzalez Alvarez, Claire Dashwood, Sophie Doward, Rahul Chahel, Emma McAllister, Sansar Raj Meena, and Filippo Catani

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Short summary
On April 2, 2024, a Mw 7.4 earthquake hit Taiwan’s eastern coast, causing extensive landslides and damage. We used automated methods combining Earth Observation (EO) data with Artificial Intelligence (AI) to quickly inventory the landslides. This approach identified 7,090 landslides over 75 km2 within 3 hours of acquiring the EO imagery. The study highlights AI’s role in improving landslide detection and understanding earthquake-landslide interactions for better hazard mitigation.
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