Earth System Science Data
Earth System Science Data
Earth System Science Data
Articles | Volume 15, issue 7
Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-3283-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-15-3283-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 15, issue 7
Data description article
 | 
27 Jul 2023
Data description article |  | 27 Jul 2023

HR-GLDD: a globally distributed dataset using generalized deep learning (DL) for rapid landslide mapping on high-resolution (HR) satellite imagery

Sansar Raj Meena, Lorenzo Nava, Kushanav Bhuyan, Silvia Puliero, Lucas Pedrosa Soares, Helen Cristina Dias, Mario Floris, and Filippo Catani

Viewed

Total article views: 7,092 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
5,118 1,819 155 7,092 198 149 176
  • HTML: 5,118
  • PDF: 1,819
  • XML: 155
  • Total: 7,092
  • Supplement: 198
  • BibTeX: 149
  • EndNote: 176
Views and downloads (calculated since 17 Oct 2022)
Cumulative views and downloads (calculated since 17 Oct 2022)

Viewed (geographical distribution)

Total article views: 7,092 (including HTML, PDF, and XML) Thereof 6,886 with geography defined and 206 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 11 Feb 2026
Download
Short summary
Landslides occur often across the world, with the potential to cause significant damage. Although a substantial amount of research has been conducted on the mapping of landslides using remote-sensing data, gaps and uncertainties remain when developing models to be operational at the global scale. To address this issue, we present the High-Resolution Global landslide Detector Database (HR-GLDD) for landslide mapping with landslide instances from 10 different physiographical regions globally.
Read more
Share
Special issue
Benchmark datasets and machine learning algorithms for Earth...
Altmetrics
Final-revised paper
Preprint