Work

Global Landslide Susceptibility Mapping Using Multi-Model Machine Learning Approaches on Geospatial Satellite Data

Year: 2025

Type: conference-paper

Abstract: This study introduces a high-resolution, global landslide susceptibility model employing a multi-model machine learning framework. Aiming to surpass the 2016 NASA model, the approach leverages an enhanced global landslide catalog (UGLC) for training, a global higher-accuracy 90m MERIT DEM, and over 100 global predictive variables encompassing topographic, geological, and environmental factors. Developed on high-performance computing for operational efficiency and scalability, the model prioritizes reliability and interpretability to support future dynamic early warning systems, linking baseline susceptibility with real-time monitoring for improved disaster response in global landslide risk assessment.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.15263707

Authors: Mancino, Saverio, Lovergine, Francesco Paolo, Capolongo, Domenico, Amatulli, Giuseppe

Institutions: University of Bari Aldo Moro, National Research Council, Yale University

Language: English

Topic: Landslides and related hazards

Subfield: Management, Monitoring, Policy and Law

Field: Environmental Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W6968367331

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