Extraction of spatial and temporal landslide information using AI

Reliable forecasting of rainfall-induced landslides requires historical data collected in structured and well-documented catalogues. However, scarce and inaccurate information on the timing and location of the failures often leads to high uncertainty in predictions. When properly trained, Artificial Intelligence (AI) can significantly accelerate data collection and processing, enabling the interpretation of large volumes of information much faster than traditional manual approaches.

We developed an AI-based two-step procedure for the automatic extraction of spatial and temporal information on rainfall-induced landslides from textual online documents. The procedure is a prompt-engineered framework, which uses Large Language Models (LLMs) and Natural Language Processing (NLP). Starting from Google Alert-filtered news on landslides, the framework integrates two-step procedure optimization for: (1) date/time attribution, (2) geolocation by combining LLM interpretative capacity with OpenStreetMap API. The output is useful for building or updating landslides catalogues, such as the ITAlian rainfall-induced LandslIdes CAtalogue (ITALICA, Peruccacci et al., 2023; Brunetti et al., 2025). This approach represents a significant advancement over traditional manual extraction of landslide information from news sources that is affected by several limitations: (1) processing of hundreds of news articles is time-consuming, complex, and highly demanding; (2) manual procedures are prone to bias and error, reducing data objectivity, reliability, and reproducibility. Moreover, (3) the heterogeneity of information sources hampers the production of standardized outputs limiting the integration into national or regional landslide catalogues. These limitations are particularly critical in operational contexts where rapid data integration is required for improving catalogue completeness, calibrating rainfall thresholds, and validating landslides early warning systems. Recent advances have partially addressed these challenges through rigorous methodologies involving multiple trained expert operators and double-validation processes (Peruccacci et al., 2023; Brunetti et al., 2025). Although expert validation remains crucial, this approach supports the reliability and objectivity of hazard modeling and prediction, contributing to global landslide research and risk reduction.

This contribution is part of the AI-PERIL (AI-Powered Extraction of Rainfall-Induced Landslide Information) project, which is supported by the International Consortium on Landslides (ICL).

 

References:

Brunetti, M.T., Gariano, S.L., Melillo, M., Rossi, M., and Peruccacci, S.: An enhanced rainfall-induced landslide catalogue in Italy. Scientific Data, 12, 216, https://doi.org/10.1038/s41597-025-04551-6, 2025

Peruccacci, S., Gariano, S. L., Melillo, M., Solimano, M., Guzzetti, F., and Brunetti, M. T.: The ITAlian rainfall-induced LandslIdes CAtalogue, an extensive and accurate spatio-temporal catalogue of rainfall-induced landslides in Italy. Earth System Science Data, 15, 2863–2877, https://doi.org/10.5194/essd-15-2863-2023, 2023.