Abstract Global climate change has led to the frequent extreme meteorological events in recent years, triggering severe clustered landslides in mountainous regions. Records of these clustered landslides not only provide post-disaster statistics but also play a crucial role in advancing data-driven regional landslide research and intelligent landslide detection. The Rainfall-induced Landslide in Zixing (RLZX) datasets consist of a landslide inventory map (LIM) and a landslide detection dataset (LDD). RLZX-LIM was created through visual interpretation of 3D scenes before and after the rainfall event, containing 19,403 shallow landslides triggered by extreme rainfall in Zixing City, China, between July 26 and July 28, 2024. We have provided quantitative evaluations of the quality of RLZX-LIM based on reference data obtained from road-aligned surveys and unmanned aerial vehicle (UAV) mapping in the field. RLZX-LDD is further developed using both UAV and satellite images, offering higher quality and robustness, effectively filling the gap in rainfall-induced LDDs. The RLZX datasets have been publicly released for free use to promote related landslide research.
Fu et al. (2025) studied this question.