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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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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