Abstract
Landslides are a major geological hazard causing significant casualties and economic losses. Reliable risk assessment requires high-quality spatiotemporal event data, yet no publicly available landslide catalogue with fine-grained precision exists for China. To address this, we developed a landslide event catalogue for mainland China from 2008–2024 based on news reports. The dataset was generated via large-scale web crawling, information extraction using an open-source large language model (LLM), event deduplication, geocoding, and multi-stage validation. It contains 1,582 events with detailed spatiotemporal attributes, some with minute-level temporal precision and spatial resolution down to the county, village, or specific reported sites. Evaluation shows that, while casualty-related information is less accurate, the LLM reliably captures key attributes such as time, location, and triggering factors. This demonstrates the feasibility of using LLMs to extract critical landslide data from news reports. Compared with existing catalogues, our dataset offers more events and improved spatiotemporal accuracy, providing a valuable resource for landslide hazard assessment, early warning model development, and disaster risk management in China.
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Data availability
The landslide event catalogue is available on figshare https://doi.org/10.6084/m9.figshare.29603420.
Code availability
The code used in this study is implemented in Python and publicly available at https://doi.org/10.6084/m9.figshare.31298212. It includes scripts for extracting landslide-related information from news reports using large language models, identifying and removing duplicate landslide event records, and performing geocoding to assign spatial coordinates to landslide events.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 42571090) and the Natural Science Foundation of Higher Education Institutions of Jiangsu Province (Grant No. 25KJB170011).
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Binru Zhao led the design of the catalogue structure, implemented the data processing workflow, and reviewed and revised the manuscript prior to final submission. Zhenxia Liu contributed to large language model–based information extraction, record processing and revisions. Lulu Zhang contributed to data collection, data analysis and the initial drafting of the manuscript. Wenchao Ma and Jian Wang contributed to data curation through manual review and revision of the extracted landslide records prior to catalogue finalization. Qiang Sun provided independent validation data for quality assessment of the catalogue. Wen Luo, Zhaoyuan Yu, and Linwang Yuan provided overall supervision and guidance on dataset design.
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Zhao, B., Zhang, L., Liu, Z. et al. A high-precision catalogue of landslide events in China based on news text mining with large language model. Sci Data (2026). https://doi.org/10.1038/s41597-026-07066-w
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DOI: https://doi.org/10.1038/s41597-026-07066-w
