Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 1
Articles | Volume 19, issue 1
https://doi.org/10.5194/gmd-19-167-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-167-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 1
Model description paper
 | 
07 Jan 2026
Model description paper |  | 07 Jan 2026

Sentinel-1 SAR-based globally distributed co-seismic landslide detection by deep neural networks

Lorenzo Nava, Alessandro Mondini, Kushanav Bhuyan, Chengyong Fang, Oriol Monserrat, Alessandro Novellino, and Filippo Catani

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Short summary
This paper presents a framework for landslide rapid detection using radar and deep learning, trained and tested on data from ≈73000 landslides across diverse regions in the world. The method showed high accuracy and rapid response potential regardless of weather and illumination conditions. By overcoming the limits of optical satellite imagery, it offers a powerful tool for timely landslide disaster response, benefiting disaster management and advancing methods for monitoring hazardous terrains.
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