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

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

doi:https://doi.org/10.5194/nhess-25-2371-2025

Articles | Volume 25, issue 7

https://doi.org/10.5194/nhess-25-2371-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/nhess-25-2371-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 25, issue 7

Brief communication

|

15 Jul 2025

Brief communication |

| 15 Jul 2025

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

Data sets

AI-driven rapid landslides mapping following the 2024 Hualien City Earthquake in Taiwan Alessandro Novellino https://doi.org/10.5281/zenodo.11519683

GDCLD C. Fang et al. https://doi.org/10.5281/zenodo.11369484

SAR-LRA: A Synthetic Aperture Radar-Based Landslide Rapid Assessment Tool Lorenzo Nava et al. https://doi.org/10.5281/zenodo.15159492

Interactive computing environment

SAR-LRA Tool V1 for Google Colaboratory Lorenzo Nava et al. https://doi.org/10.5281/zenodo.14898555

Short summary

On 2 April 2024, a M_w 7.4 earthquake hit Taiwan's eastern coast, causing extensive landslides and damage. We used automated methods combining Earth observation (EO) data with AI to quickly inventory the landslides. This approach identified 7090 landslides over 75 km² within 3 h of acquiring the EO imagery. The study highlights AI's role in improving landslide detection efforts in disaster response.