Articles | Volume 16, issue 10
https://doi.org/10.5194/essd-16-4817-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-16-4817-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
24 Oct 2024
Data description article |
| 24 Oct 2024
| 24 Oct 2024
A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images
Chengyong Fang
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, 610059 Chengdu, China
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, 610059 Chengdu, China
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, 610059 Chengdu, China
Lorenzo Nava
Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padua, 35129 Padua, Italy
Hao Zhong
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, 610059 Chengdu, China
College of Information Science and Technology, Chengdu University of Technology, 610059 Chengdu, China
Xiujun Dong
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, 610059 Chengdu, China
Jixiao Qi
State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, 610059 Chengdu, China
Filippo Catani
Machine Intelligence and Slope Stability Laboratory, Department of Geosciences, University of Padua, 35129 Padua, Italy
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Cited
46 citations as recorded by crossref.
- A Spatio-Temporal Dataset for Satellite-Based Landslide Detection P. Höhn et al. https://doi.org/10.1038/s41597-025-06167-2
- Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors R. Wei et al. https://doi.org/10.3390/rs17152635
- Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions X. Chen et al. https://doi.org/10.3390/rs17111856
- FMEformer: a frequency-domain enhanced transformer for cross-domain landslide extraction L. Wang et al. https://doi.org/10.1080/17538947.2026.2677973
- A lightweight Dual-Stream Attention Network for real-time landslide monitoring in multi-modal remote sensing imagery P. Dhayal et al. https://doi.org/10.1016/j.rsase.2025.101732
- CRLMDG-LM: Causal representation learning-guided multi-target domain generalization network for fine-grained landslide mapping from high-resolution remote sensing images C. Zhao et al. https://doi.org/10.1016/j.knosys.2025.114796
- Mapping global post-earthquake ecosystem damage boundaries W. He et al. https://doi.org/10.1016/j.gsf.2026.102288
- ConvNeXt with Context-Weighted Deep Superpixels for High-Spatial-Resolution Aerial Image Semantic Segmentation Z. Ye et al. https://doi.org/10.3390/ai6110277
- Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan L. Nava et al. https://doi.org/10.5194/nhess-25-2371-2025
- A CNN–Transformer hybrid network for efficient cross-region landslide detection by transfer learning Z. Fu et al. https://doi.org/10.1007/s10346-026-02733-w
- A Cross-Domain Landslide Extraction Method Utilizing Image Masking and Morphological Information Enhancement J. Chen et al. https://doi.org/10.3390/rs17081464
- CGHD: Dual-Temporal Dataset of Composite Geological Hazards via Multi-Source Optical Remote Sensing Images Y. Wang et al. https://doi.org/10.3390/rs18081198
- SUGARFuseNet: Diffusion‑driven domain adaptation and bimodal bitemporal fusion for advancing global landslide segmentation on novel GBMT‑SLID dataset G. Emani et al. https://doi.org/10.1016/j.neucom.2026.134060
- From Landslide Detection to Multi-Source LLM-Based Reporting: A Complete Framework for Rapid Assessment of Post-Disaster Scenarios M. Alruqimi et al. https://doi.org/10.3390/rs18111821
- Hanging wall effects on cross-fault slope failures: Shaking table experiment insights T. Wei et al. https://doi.org/10.1016/j.enggeo.2025.107985
- Landslide susceptibility on the Qinghai-Tibet Plateau: Key driving factors identified through machine learning W. Yang et al. https://doi.org/10.1007/s11442-026-2444-6
- Improved Flood Insights: Diffusion-Based SAR-to-EO Image Translation M. Seo et al. https://doi.org/10.3390/rs17132260
- Space-time variability modelling of landslide susceptibility for strategic infrastructure under changing climate scenarios: The case study of the mega clean energy transmission network (Yangtze River Basin, China) B. Jin et al. https://doi.org/10.1016/j.enggeo.2026.108738
- MSRS-MambaUNet: A multi-source remote sensing model for landslide detection Z. Zhang et al. https://doi.org/10.1016/j.jrmge.2026.03.020
- Deep learning unlocks global prediction of earthquake-triggered landslides F. Catani https://doi.org/10.1093/nsr/nwaf282
- CAGM-Seg: A Symmetry-Driven Lightweight Model for Small Object Detection in Multi-Scenario Remote Sensing H. Yao et al. https://doi.org/10.3390/sym17122137
- A workflow to identify and monitor slow-moving landslides through spaceborne optical feature tracking L. Nava et al. https://doi.org/10.5194/nhess-26-2305-2026
- LMHLD: A Large-Scale Multisource High-Resolution Landslide Dataset for Landslide Detection Based on Deep Learning G. Liu et al. https://doi.org/10.1109/TGRS.2025.3619062
- Rapid and robust landslide mapping from optical EO imagery using a mamba-based deep learning framework C. Fang et al. https://doi.org/10.1007/s10346-026-02789-8
- Dual-encoder multiscale transformer fusion network for landslide detection integrating Sentinel-2 spectral and topographic clues X. Gao & P. Lu https://doi.org/10.1007/s10346-026-02768-z
- Cross-Domain Landslide Mapping in Remote Sensing Images Based on Unsupervised Domain Adaptation Framework J. Yang et al. https://doi.org/10.3390/rs18020286
- Understanding Landslide Expression in SAR Backscatter Data: Global Study and Disaster Response Application E. Lindsay et al. https://doi.org/10.3390/rs17193313
- Cross-domain coseismic landslide segmentation: local boundary enhancement & global pixel contrastive learning X. He et al. https://doi.org/10.1080/19475705.2026.2623104
- Lights-Transformer: An Efficient Transformer-Based Landslide Detection Model for High-Resolution Remote Sensing Images X. Wu et al. https://doi.org/10.3390/s25123646
- Research on a spatial multi-scale detection method for newly occurred landslides by coupling LGBM and YOLO X. Yu & Z. Wang https://doi.org/10.1007/s11069-026-08134-5
- CSLMamba-LM: Mamba-based causal self-contrastive learning network for the fine-grained landslide mapping from very-high-resolution aerial images C. Zhao et al. https://doi.org/10.1016/j.eswa.2025.128669
- High-Resolution (0.9 m) terrace mapping in Low-Latitude hilly regions using deep learning and Area–Slope Denoising: A case study from Guangdong Province, China Y. Zhao et al. https://doi.org/10.1016/j.jag.2026.105126
- Change detection-based machine learning for earthquake-triggered landslide identification in complex mountain landscapes L. Wang et al. https://doi.org/10.1016/j.geomorph.2026.110364
- Hillslope torrential hazard cascades in tropical mountains M. Arango-Carmona et al. https://doi.org/10.5194/nhess-25-3641-2025
- A benchmark dataset and baseline methods for rock microstructure interpretation in SEM images Y. Zhang et al. https://doi.org/10.1038/s41597-025-05947-0
- Exploring Infrared and Visible Feature Fusion for Earth Surface Anomaly Detection: From Benchmark Dataset to Spatial-Spectrum Adaptation Network L. Wang et al. https://doi.org/10.1109/TGRS.2026.3668268
- Review article: Deep learning for potential landslide identification: data, models, applications, challenges, and opportunities P. Jiang et al. https://doi.org/10.5194/nhess-26-487-2026
- A multi-modal deep learning framework with GAN-based fusion for enhanced landslide detection R. Srivats et al. https://doi.org/10.1371/journal.pone.0347324
- LSDSAM: Harnessing Visual Foundation Model and Enhanced Transfer Learning Toward Practical Landslide Detection in Few-Shot Scenarios Z. Fu et al. https://doi.org/10.1109/JSTARS.2026.3678141
- Towards intelligent landslide susceptibility evaluation: Knowledge extraction and rule mining X. Yang et al. https://doi.org/10.1016/j.knosys.2025.114656
- Sentinel-1 SAR-based globally distributed co-seismic landslide detection by deep neural networks L. Nava et al. https://doi.org/10.5194/gmd-19-167-2026
- Enhancing Sentinel-2 landslide change detection by integrating multispectral, deformation, and topographic information B. Liu et al. https://doi.org/10.1016/j.jag.2026.105116
- An Improved Geospatial Object Detection Framework for Complex Urban and Environmental Remote Sensing Scenes Y. Zhu et al. https://doi.org/10.3390/app16031288
- CICRL-FLM: counterfactual inference causal representation learning network for fine-grained landslide mapping from satellite remote sensing images C. Zhao et al. https://doi.org/10.1080/15481603.2025.2598078
- A style-Pix2Pix GAN framework for data augmentation in landslide semantic segmentation T. Ren et al. https://doi.org/10.1007/s10346-025-02621-9
- Uncertainty-Aware Label-Efficient Landslide Segmentation in Open-Pit Mines via Transformer Transfer Learning and Active Learning H. Li et al. https://doi.org/10.3390/rs18111774
46 citations as recorded by crossref.
- A Spatio-Temporal Dataset for Satellite-Based Landslide Detection P. Höhn et al. https://doi.org/10.1038/s41597-025-06167-2
- Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors R. Wei et al. https://doi.org/10.3390/rs17152635
- Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions X. Chen et al. https://doi.org/10.3390/rs17111856
- FMEformer: a frequency-domain enhanced transformer for cross-domain landslide extraction L. Wang et al. https://doi.org/10.1080/17538947.2026.2677973
- A lightweight Dual-Stream Attention Network for real-time landslide monitoring in multi-modal remote sensing imagery P. Dhayal et al. https://doi.org/10.1016/j.rsase.2025.101732
- CRLMDG-LM: Causal representation learning-guided multi-target domain generalization network for fine-grained landslide mapping from high-resolution remote sensing images C. Zhao et al. https://doi.org/10.1016/j.knosys.2025.114796
- Mapping global post-earthquake ecosystem damage boundaries W. He et al. https://doi.org/10.1016/j.gsf.2026.102288
- ConvNeXt with Context-Weighted Deep Superpixels for High-Spatial-Resolution Aerial Image Semantic Segmentation Z. Ye et al. https://doi.org/10.3390/ai6110277
- Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan L. Nava et al. https://doi.org/10.5194/nhess-25-2371-2025
- A CNN–Transformer hybrid network for efficient cross-region landslide detection by transfer learning Z. Fu et al. https://doi.org/10.1007/s10346-026-02733-w
- A Cross-Domain Landslide Extraction Method Utilizing Image Masking and Morphological Information Enhancement J. Chen et al. https://doi.org/10.3390/rs17081464
- CGHD: Dual-Temporal Dataset of Composite Geological Hazards via Multi-Source Optical Remote Sensing Images Y. Wang et al. https://doi.org/10.3390/rs18081198
- SUGARFuseNet: Diffusion‑driven domain adaptation and bimodal bitemporal fusion for advancing global landslide segmentation on novel GBMT‑SLID dataset G. Emani et al. https://doi.org/10.1016/j.neucom.2026.134060
- From Landslide Detection to Multi-Source LLM-Based Reporting: A Complete Framework for Rapid Assessment of Post-Disaster Scenarios M. Alruqimi et al. https://doi.org/10.3390/rs18111821
- Hanging wall effects on cross-fault slope failures: Shaking table experiment insights T. Wei et al. https://doi.org/10.1016/j.enggeo.2025.107985
- Landslide susceptibility on the Qinghai-Tibet Plateau: Key driving factors identified through machine learning W. Yang et al. https://doi.org/10.1007/s11442-026-2444-6
- Improved Flood Insights: Diffusion-Based SAR-to-EO Image Translation M. Seo et al. https://doi.org/10.3390/rs17132260
- Space-time variability modelling of landslide susceptibility for strategic infrastructure under changing climate scenarios: The case study of the mega clean energy transmission network (Yangtze River Basin, China) B. Jin et al. https://doi.org/10.1016/j.enggeo.2026.108738
- MSRS-MambaUNet: A multi-source remote sensing model for landslide detection Z. Zhang et al. https://doi.org/10.1016/j.jrmge.2026.03.020
- Deep learning unlocks global prediction of earthquake-triggered landslides F. Catani https://doi.org/10.1093/nsr/nwaf282
- CAGM-Seg: A Symmetry-Driven Lightweight Model for Small Object Detection in Multi-Scenario Remote Sensing H. Yao et al. https://doi.org/10.3390/sym17122137
- A workflow to identify and monitor slow-moving landslides through spaceborne optical feature tracking L. Nava et al. https://doi.org/10.5194/nhess-26-2305-2026
- LMHLD: A Large-Scale Multisource High-Resolution Landslide Dataset for Landslide Detection Based on Deep Learning G. Liu et al. https://doi.org/10.1109/TGRS.2025.3619062
- Rapid and robust landslide mapping from optical EO imagery using a mamba-based deep learning framework C. Fang et al. https://doi.org/10.1007/s10346-026-02789-8
- Dual-encoder multiscale transformer fusion network for landslide detection integrating Sentinel-2 spectral and topographic clues X. Gao & P. Lu https://doi.org/10.1007/s10346-026-02768-z
- Cross-Domain Landslide Mapping in Remote Sensing Images Based on Unsupervised Domain Adaptation Framework J. Yang et al. https://doi.org/10.3390/rs18020286
- Understanding Landslide Expression in SAR Backscatter Data: Global Study and Disaster Response Application E. Lindsay et al. https://doi.org/10.3390/rs17193313
- Cross-domain coseismic landslide segmentation: local boundary enhancement & global pixel contrastive learning X. He et al. https://doi.org/10.1080/19475705.2026.2623104
- Lights-Transformer: An Efficient Transformer-Based Landslide Detection Model for High-Resolution Remote Sensing Images X. Wu et al. https://doi.org/10.3390/s25123646
- Research on a spatial multi-scale detection method for newly occurred landslides by coupling LGBM and YOLO X. Yu & Z. Wang https://doi.org/10.1007/s11069-026-08134-5
- CSLMamba-LM: Mamba-based causal self-contrastive learning network for the fine-grained landslide mapping from very-high-resolution aerial images C. Zhao et al. https://doi.org/10.1016/j.eswa.2025.128669
- High-Resolution (0.9 m) terrace mapping in Low-Latitude hilly regions using deep learning and Area–Slope Denoising: A case study from Guangdong Province, China Y. Zhao et al. https://doi.org/10.1016/j.jag.2026.105126
- Change detection-based machine learning for earthquake-triggered landslide identification in complex mountain landscapes L. Wang et al. https://doi.org/10.1016/j.geomorph.2026.110364
- Hillslope torrential hazard cascades in tropical mountains M. Arango-Carmona et al. https://doi.org/10.5194/nhess-25-3641-2025
- A benchmark dataset and baseline methods for rock microstructure interpretation in SEM images Y. Zhang et al. https://doi.org/10.1038/s41597-025-05947-0
- Exploring Infrared and Visible Feature Fusion for Earth Surface Anomaly Detection: From Benchmark Dataset to Spatial-Spectrum Adaptation Network L. Wang et al. https://doi.org/10.1109/TGRS.2026.3668268
- Review article: Deep learning for potential landslide identification: data, models, applications, challenges, and opportunities P. Jiang et al. https://doi.org/10.5194/nhess-26-487-2026
- A multi-modal deep learning framework with GAN-based fusion for enhanced landslide detection R. Srivats et al. https://doi.org/10.1371/journal.pone.0347324
- LSDSAM: Harnessing Visual Foundation Model and Enhanced Transfer Learning Toward Practical Landslide Detection in Few-Shot Scenarios Z. Fu et al. https://doi.org/10.1109/JSTARS.2026.3678141
- Towards intelligent landslide susceptibility evaluation: Knowledge extraction and rule mining X. Yang et al. https://doi.org/10.1016/j.knosys.2025.114656
- Sentinel-1 SAR-based globally distributed co-seismic landslide detection by deep neural networks L. Nava et al. https://doi.org/10.5194/gmd-19-167-2026
- Enhancing Sentinel-2 landslide change detection by integrating multispectral, deformation, and topographic information B. Liu et al. https://doi.org/10.1016/j.jag.2026.105116
- An Improved Geospatial Object Detection Framework for Complex Urban and Environmental Remote Sensing Scenes Y. Zhu et al. https://doi.org/10.3390/app16031288
- CICRL-FLM: counterfactual inference causal representation learning network for fine-grained landslide mapping from satellite remote sensing images C. Zhao et al. https://doi.org/10.1080/15481603.2025.2598078
- A style-Pix2Pix GAN framework for data augmentation in landslide semantic segmentation T. Ren et al. https://doi.org/10.1007/s10346-025-02621-9
- Uncertainty-Aware Label-Efficient Landslide Segmentation in Open-Pit Mines via Transformer Transfer Learning and Active Learning H. Li et al. https://doi.org/10.3390/rs18111774
Saved (final revised paper)
Latest update: 13 Jun 2026
Short summary
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.
In this study, we present the largest publicly available landslide dataset, Globally Distributed...
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