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.
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.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
07 Jan 2026
Model description paper |
| 07 Jan 2026
| 07 Jan 2026
Sentinel-1 SAR-based globally distributed co-seismic landslide detection by deep neural networks
Department of Earth Sciences, University of Cambridge, Cambridge, UK
Department of Geography, University of Cambridge, Cambridge, UK
Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padova, Padova, Italy
Alessandro Mondini
National Research Council, Research Institute for Applied Mathematics and Information Technologies, Genova, Italy
Kushanav Bhuyan
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, University of Technology, Chengdu, China
Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padova, Padova, Italy
Chengyong Fang
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, University of Technology, Chengdu, China
Oriol Monserrat
Department of Remote Sensing, Geomatics Research Unit, Centre Tecnologic de Telecomunicacions de Catalunya (CTTC), Barcelona, Spain
Alessandro Novellino
British Geological Survey, Keyworth, Nottingham, UK
Filippo Catani
Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padova, Padova, Italy
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Nat. Hazards Earth Syst. Sci., 25, 2371–2377, https://doi.org/10.5194/nhess-25-2371-2025, https://doi.org/10.5194/nhess-25-2371-2025, 2025
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On 2 April 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 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.
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In this study, we present the largest publicly available landslide dataset, Globally Distributed Coseismic Landslide Dataset (GDCLD), which includes multi-sensor high-resolution images from various locations around the world. We test GDCLD with seven advanced algorithms and show that it is effective in achieving reliable landslide mapping across different triggers and environments, with great potential in enhancing emergency response and disaster management.
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Landslides occur often across the world, with the potential to cause significant damage. Although a substantial amount of research has been conducted on the mapping of landslides using remote-sensing data, gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD) for landslide mapping with landslide instances from 10 different physiographical regions globally.
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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On 2 April 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 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.
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We introduce TerraTrack, an open-source tool for detecting and monitoring slow-moving landslides using Sentinel-2 data. It automates image acquisition, landslide identification, and time-series generation in an accessible and cloud-based workflow. TerraTrack supports early warning, complements InSAR, and offers a scalable solution for landslide hazard identification and monitoring.
Oriol Monserrat, Anna Barra, Marta Béjar-Pizarro, Jonathan S. Rivera, Jorge Pedro Galve, Carolina Guardiola, Maria Cuevas-González, Rosa Maria Mateos, Pablo Ezquerro, Jose Miguel Azañon, Saeedeh Shahbazi, Jose Navarro, Michele Crosetto, and Guido Luzi
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 351–356, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-351-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-351-2024, 2024
Maria Carmelia Ramlie, Oriol Monserrat, Bruno Crippa, Paula Olea-Encina, and Michele Crosetto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-2024, 459–464, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-459-2024, https://doi.org/10.5194/isprs-archives-XLVIII-3-2024-459-2024, 2024
Chengyong Fang, Xuanmei Fan, Xin Wang, Lorenzo Nava, Hao Zhong, Xiujun Dong, Jixiao Qi, and Filippo Catani
Earth Syst. Sci. Data, 16, 4817–4842, https://doi.org/10.5194/essd-16-4817-2024, https://doi.org/10.5194/essd-16-4817-2024, 2024
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In this study, we present the largest publicly available landslide dataset, Globally Distributed Coseismic Landslide Dataset (GDCLD), which includes multi-sensor high-resolution images from various locations around the world. We test GDCLD with seven advanced algorithms and show that it is effective in achieving reliable landslide mapping across different triggers and environments, with great potential in enhancing emergency response and disaster management.
Sansar Raj Meena, Lorenzo Nava, Kushanav Bhuyan, Silvia Puliero, Lucas Pedrosa Soares, Helen Cristina Dias, Mario Floris, and Filippo Catani
Earth Syst. Sci. Data, 15, 3283–3298, https://doi.org/10.5194/essd-15-3283-2023, https://doi.org/10.5194/essd-15-3283-2023, 2023
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Landslides occur often across the world, with the potential to cause significant damage. Although a substantial amount of research has been conducted on the mapping of landslides using remote-sensing data, gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD) for landslide mapping with landslide instances from 10 different physiographical regions globally.
Guillermo Tamburini-Beliveau, Sebastián Balbarani, and Oriol Monserrat
Nat. Hazards Earth Syst. Sci., 23, 1987–1999, https://doi.org/10.5194/nhess-23-1987-2023, https://doi.org/10.5194/nhess-23-1987-2023, 2023
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Landslides and ground deformation associated with the construction of a hydropower mega dam in the Santa Cruz River in Argentine Patagonia have been monitored using radar and optical satellite data, together with the analysis of technical reports. This allowed us to assess the integrity of the construction, providing a new and independent dataset. We have been able to identify ground deformation trends that put the construction works at risk.
Txomin Bornaetxea, Ivan Marchesini, Sumit Kumar, Rabisankar Karmakar, and Alessandro Mondini
Nat. Hazards Earth Syst. Sci., 22, 2929–2941, https://doi.org/10.5194/nhess-22-2929-2022, https://doi.org/10.5194/nhess-22-2929-2022, 2022
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One cannot know if there is a landslide or not in an area that one has not observed. This is an obvious statement, but when landslide inventories are obtained by field observation, this fact is seldom taken into account. Since fieldwork campaigns are often done following the roads, we present a methodology to estimate the visibility of the terrain from the roads, and we demonstrate that fieldwork-based inventories are underestimating landslide density in less visible areas.
Angelica Tarpanelli, Alessandro C. Mondini, and Stefania Camici
Nat. Hazards Earth Syst. Sci., 22, 2473–2489, https://doi.org/10.5194/nhess-22-2473-2022, https://doi.org/10.5194/nhess-22-2473-2022, 2022
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We analysed 10 years of river discharge data from almost 2000 sites in Europe, and we extracted flood events, as proxies of flood inundations, based on the overpasses of Sentinel-1 and Sentinel-2 satellites to derive the percentage of potential inundation events that they were able to observe. Results show that on average 58 % of flood events are potentially observable by Sentinel-1 and only 28 % by Sentinel-2 due to the obstacle of cloud coverage.
M. Crosetto, L. Solari, A. Barra, O. Monserrat, M. Cuevas-González, R. Palamà, Y. Wassie, S. Shahbazi, S. M. Mirmazloumi, B. Crippa, and M. Mróz
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 257–262, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-257-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-257-2022, 2022
Q. Gao, M. Crosetto, O. Monserrat, R. Palama, and A. Barra
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 271–276, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-271-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-271-2022, 2022
S. M. Mirmazloumi, Á. F. Gambin, Y. Wassie, A. Barra, R. Palamà, M. Crosetto, O. Monserrat, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 307–312, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-307-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-307-2022, 2022
J. A. Navarro, D. García, M. Crosetto, and O. Monserrat
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 313–320, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-313-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-313-2022, 2022
R. Palamà, M. Crosetto, O. Monserrat, A. Barra, B. Crippa, M. Mróz, N. Kotulak, M. Mleczko, and J. Rapinski
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 321–326, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-321-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-321-2022, 2022
Y. Wassie, Q. Gao, O. Monserrat, A. Barra, B. Crippa, and M. Crosetto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 361–366, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-361-2022, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-361-2022, 2022
Andrea Manconi, Alessandro C. Mondini, and the AlpArray working group
Nat. Hazards Earth Syst. Sci., 22, 1655–1664, https://doi.org/10.5194/nhess-22-1655-2022, https://doi.org/10.5194/nhess-22-1655-2022, 2022
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Information on when, where, and how landslide events occur is the key to building complete catalogues and performing accurate hazard assessments. Here we show a procedure that allows us to benefit from the increased density of seismic sensors installed on ground for earthquake monitoring and from the unprecedented availability of satellite radar data. We show how the procedure works on a recent sequence of landslides that occurred at Piz Cengalo (Swiss Alps) in 2017.
Sansar Raj Meena, Silvia Puliero, Kushanav Bhuyan, Mario Floris, and Filippo Catani
Nat. Hazards Earth Syst. Sci., 22, 1395–1417, https://doi.org/10.5194/nhess-22-1395-2022, https://doi.org/10.5194/nhess-22-1395-2022, 2022
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The study investigated the importance of the conditioning factors in predicting landslide occurrences using the mentioned models. In this paper, we evaluated the importance of the conditioning factors (features) in the overall prediction capabilities of the statistical and machine learning algorithms.
J. A. Navarro, A. Barra, O. Monserrat, and M. Crosetto
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 163–169, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-163-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-163-2021, 2021
Y. Wassie, M. Crosetto, G. Luzi, O. Monserrat, A. Barra, R. Palamá, M. Cuevas-González, S. M. Mirmazloumi, P. Espín-López, and B. Crippa
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 177–182, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-177-2021, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-177-2021, 2021
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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.
This paper presents a framework for landslide rapid detection using radar and deep learning,...
