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

Abstract

Rapid and accurate mapping of landslides triggered by extreme events is essential for effective emergency response, hazard mitigation, and disaster management. However, the development of generalized machine learning models for landslide detection has been hindered by the absence of a high-resolution, globally distributed, event-based dataset. To address this gap, we introduce the Globally Distributed Coseismic Landslide Dataset (GDCLD), a comprehensive dataset that integrates multi-source remote sensing images, including PlanetScope, Gaofen-6, Map World, and uncrewed aerial vehicle (UAV) data, with varying geographical and geological background for nine events across the globe. The GDCLD data are freely available at https://doi.org/10.5281/zenodo.13612636 (Fang et al., 2024). In this study, we evaluated the effectiveness of GDCLD by comparing the mapping performance of seven state-of-the-art semantic segmentation algorithms. These models were further tested by three different types of remote sensing images in four independent regions, with the GDCLD-SegFormer model achieving the best performance. Additionally, we extended the evaluation to a rainfall-induced landslide dataset, where the models demonstrated excellent performance as well, highlighting the dataset’s applicability to landslide segmentation triggered by other factors. Our results confirm the superiority of GDCLD in remote sensing landslide detection modeling, offering a comprehensive database for rapid landslide assessment following future unexpected events worldwide.