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 paper
| 
24 Oct 2024
Data description paper |
| 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
10 citations as recorded by crossref.
- A Cross-Domain Landslide Extraction Method Utilizing Image Masking and Morphological Information Enhancement J. Chen et al. 10.3390/rs17081464
- Lights-Transformer: An Efficient Transformer-Based Landslide Detection Model for High-Resolution Remote Sensing Images X. Wu et al. 10.3390/s25123646
- Hanging wall effects on cross-fault slope failures: Shaking table experiment insights T. Wei et al. 10.1016/j.enggeo.2025.107985
- Improved Flood Insights: Diffusion-Based SAR-to-EO Image Translation M. Seo et al. 10.3390/rs17132260
- 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. 10.1016/j.eswa.2025.128669
- Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors R. Wei et al. 10.3390/rs17152635
- Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions X. Chen et al. 10.3390/rs17111856
- Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan L. Nava et al. 10.5194/nhess-25-2371-2025
- Landslide detection based on pixel-level contrastive learning for semi-supervised semantic segmentation in wide areas J. Lv et al. 10.1007/s10346-024-02425-3
- A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images C. Fang et al. 10.5194/essd-16-4817-2024
8 citations as recorded by crossref.
- A Cross-Domain Landslide Extraction Method Utilizing Image Masking and Morphological Information Enhancement J. Chen et al. 10.3390/rs17081464
- Lights-Transformer: An Efficient Transformer-Based Landslide Detection Model for High-Resolution Remote Sensing Images X. Wu et al. 10.3390/s25123646
- Hanging wall effects on cross-fault slope failures: Shaking table experiment insights T. Wei et al. 10.1016/j.enggeo.2025.107985
- Improved Flood Insights: Diffusion-Based SAR-to-EO Image Translation M. Seo et al. 10.3390/rs17132260
- 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. 10.1016/j.eswa.2025.128669
- Clustered Rainfall-Induced Landslides in Jiangwan Town, Guangdong, China During April 2024: Characteristics and Controlling Factors R. Wei et al. 10.3390/rs17152635
- Harnessing Geospatial Artificial Intelligence and Deep Learning for Landslide Inventory Mapping: Advances, Challenges, and Emerging Directions X. Chen et al. 10.3390/rs17111856
- Brief communication: AI-driven rapid landslide mapping following the 2024 Hualien earthquake in Taiwan L. Nava et al. 10.5194/nhess-25-2371-2025
2 citations as recorded by crossref.
- Landslide detection based on pixel-level contrastive learning for semi-supervised semantic segmentation in wide areas J. Lv et al. 10.1007/s10346-024-02425-3
- A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images C. Fang et al. 10.5194/essd-16-4817-2024
Latest update: 07 Aug 2025
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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