Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 16, issue 10
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

Viewed

Total article views: 1,841 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
1,167 339 335 1,841 59 22 18
  • HTML: 1,167
  • PDF: 339
  • XML: 335
  • Total: 1,841
  • Supplement: 59
  • BibTeX: 22
  • EndNote: 18
Views and downloads (calculated since 18 Jul 2024)
Cumulative views and downloads (calculated since 18 Jul 2024)

Viewed (geographical distribution)

Total article views: 1,841 (including HTML, PDF, and XML) Thereof 1,776 with geography defined and 65 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 20 Nov 2024
Download
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.
Read more
Altmetrics
Final-revised paper
Preprint