Evidence map›Paper›PMID 41862505›Full record

ArticleScientific data2026

A high-precision catalogue of landslide events in China based on news text mining with large language model.

Binru Zhao, Lulu Zhang, Zhenxia Liu, Wenchao Ma, Jian Wang, Qiang Sun, Wen Luo, Zhaoyuan Yu, Linwang Yuan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Binru ZhaoState Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, 210023, China.
Lulu ZhangSchool of Geography, Nanjing Normal University, Nanjing, 210023, China.
Zhenxia LiuState Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, 210023, China. liuzhenxia@nnu.edu.cn.
Wenchao MaState Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, 210023, China.
Jian WangSchool of Geography, Nanjing Normal University, Nanjing, 210023, China.
Qiang SunNanjing Center, China Geological Survey, Nanjing, 210016, China.
Wen LuoState Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, 210023, China.
Zhaoyuan YuState Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, 210023, China.ORCID http://orcid.org/0000-0003-4225-9435
Linwang YuanState Key Laboratory of Climate System Prediction and Risk Management, Nanjing Normal University, Nanjing, 210023, China.

Funding

National Natural Science Foundation of China (National Science Foundation of China) 42571090
6 · The paper itself

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.

Identifiers

PMID41862505
PMCPMC13168302

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.