A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images

Fang, Chengyong; Fan, Xuanmei; Wang, Xin; Nava, Lorenzo; Zhong, Hao; Dong, Xiujun; Qi, Jixiao; Catani, Filippo

doi:https://doi.org/10.5194/essd-16-4817-2024

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.

https://doi.org/10.5194/essd-16-4817-2024

© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 16, issue 10

Data description paper

|

24 Oct 2024

Data description paper |

| 24 Oct 2024

A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images

Chengyong Fang, Xuanmei Fan, Xin Wang, Lorenzo Nava, Hao Zhong, Xiujun Dong, Jixiao Qi, and Filippo Catani

Supplement

https://doi.org/10.5194/essd-16-4817-2024-supplement

Data sets

GDCLD:A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images Chengyong Fang et al. https://doi.org/10.5281/zenodo.13612636

Model code and software

GDCLD: A globally distributed dataset of coseismic landslide mapping via multi-source high-resolution remote sensing images (the code) Chengyong Fang https://doi.org/10.5281/zenodo.13956757

Download

Article (21278 KB)
Full-text XML

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.