Articles | Volume 15, issue 7
https://doi.org/10.5194/essd-15-3283-2023
https://doi.org/10.5194/essd-15-3283-2023
Data description paper
 | 
27 Jul 2023
Data description paper |  | 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

Data sets

HR-GLDD Sansar Raj Meena, Lorenzo nava, Kushanav Bhuyan, Silvia Puliero, Lucas Pedrosa Soares, Helen Cristina Dias, Mario Floris, and Filippo Catani https://doi.org/10.5281/zenodo.7189381

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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.
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