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

Abstract. Rapid and accurate landslide mapping following extreme triggering events is critical for emergency response, hazard prevention, and disaster management. Artificial intelligence- based approaches enable rapid landslide mapping, yet the lack of a high-resolution globally distributed and event-based dataset poses a severe challenge in developing generalized machine learning models for landslide detection. This paper addresses this issue by designing a diverse coseismic landslide dataset, the Globally Distributed Coseismic Landslide Dataset (GDCLD), which includes multi-source remote sensing images (i.e., PlanetScope, Gaofen-6, Map World, and Unmanned Aerial Vehicle) encompassing various geographical and geological backgrounds worldwide. The GDCLD can be accessed through this link: https://doi.org/10.5281/zenodo.11369484 (Fang et al., 2024). Furthermore, we evaluate the potential of GDCLD by analyzing mapping performance of the seven most popular semantic segmentation algorithms. We further validate the generalization capabilities of the dataset by deploying the models on three types of remote sensing images from four independent regions. Besides, we also assess the model on rainfall-induced landslide dataset and achieve good results, demonstrating its applicability in landslide segmentation under other triggering factors. The results indicate the superiority of the proposed dataset in landslide detection, offering a robust mapping solution for rapid assessment in future extreme events that trigger landslides across the globe.