the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sentinel-1 SAR-based Globally Distributed Co-Seismic Landslide Detection by Deep Neural Networks
Abstract. Rapid response to multiple landslide events (MLEs) demands accurate, all-weather, day-and-night detection capabilities. Optical remote sensing has advanced landslide detection but remains limited under adverse weather and lighting conditions. Synthetic Aperture Radar (SAR), resilient to these constraints, remains underexplored for automated landslide detection due to challenges such as complex pre-processing and geometric distortions. This study integrates Deep Neural Networks (DNNs) with SAR backscatter data for co-seismic landslide detection, utilizing a data-centric approach. We inform the models using 11 earthquake-induced MLEs, covering ≈ 73000 landslides across diverse geologic and climatic settings. Inference on unseen MLEs in Haiti (2021) and Sumatra (2022) demonstrates robust transferability, achieving F1-scores up to 82 %. Using explainable artificial intelligence, we highlight the discriminative capability of change detection bands over backscatter alone. Our findings emphasize the potential of SAR-based DNN models for worldwide, generalized, and rapid landslide detection, addressing critical gaps in current methods that solely use optical data. This research lays a foundation for broader applications in automated SAR-based earth surface change detection, particularly in complex, hilly and mountainous terrains.
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Status: open (until 23 Apr 2025)
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RC1: 'Comment on gmd-2024-230', Anonymous Referee #1, 24 Mar 2025
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See attached pdf for comments
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CEC1: 'Comment on gmd-2024-230', Astrid Kerkweg, 04 Apr 2025
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Dear Authors,
it seems to me that you are using here a lot of datasets from different sources, and some processing with Google Earth Engine.
Please share the input data in an acceptable repository. If there are data products you are not able to share for good reasons, provide these reasons in the data availability section.
Best regards, Astrid Kerkweg (GMD executive Editor)
Citation: https://doi.org/10.5194/gmd-2024-230-CEC1 -
AC1: 'Reply on CEC1', Lorenzo Nava, 06 Apr 2025
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Dear Astrid Kerkweg,
Thank you for your comment.
We have now updated the Zenodo repository to ensure all relevant input data are properly archived and accessible (https://doi.org/10.5281/zenodo.15159492). The repository includes:
- Shapefiles of all inventories used in the analysis
- SAR datasets used for the rapid assessment combination
- Model weights
- Processing code (already previously available)
We hope this addresses your concerns and are happy to provide further clarifications if needed. We will update the Data Availability section of the manuscript accordingly in the first review round.
Best regards,
Lorenzo NavaCitation: https://doi.org/10.5194/gmd-2024-230-AC1
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AC1: 'Reply on CEC1', Lorenzo Nava, 06 Apr 2025
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Model code and software
SAR-LRA: A Synthetic Aperture Radar-Based Landslide Rapid Assessment Tool Lorenzo Nava, Alessandro Mondini, Kushanav Bhuyan, Chengyong Fang, Oriol Monserrat, Alessandro Novellino, and Filippo Catani https://zenodo.org/records/14898556
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