Automatic landslide prioritization at regional scale through PS-InSAR cluster analysis and socio-economic impacts

Abstract

The 2016-2017 seismic sequence in the Central Apennines (Italy) necessitated a comprehensive revision of the Hydrogeological Asset Plans landslide database to support post-seismic reconstruction. To address this critical need for updated risk assessment, this study aims to develop and validate an automated workflow for classifying and prioritizing landslide-prone areas, providing government institutions with a systematic approach to landslide risk assessment. Our innovative methodology integrates multi-sensor Persistent Scatterers (PS) interferometric data, advanced clustering techniques, and socio-economic factors to establish a standardized procedure for monitoring hazardous areas and optimizing resource allocation. The multi-sensor analysis reveals that approximately 6% of landslides are undetectable by interferometric technique, 45% show stability with no PS-detected deformation, and 19% are accurately mapped with deformation confined within their boundaries. Notably, 30% of analyzed landslides exhibit displacement beyond their mapped perimeters, indicating potential expansion or underestimation of their extent. This comprehensive classification enables authorities to identify and prioritize critical areas requiring immediate intervention based on hazard levels and socio-economic impact. The study concludes that this multi-sensor approach significantly enhances the efficiency of field inspections and territorial planning by providing a data-driven framework for intervention prioritization, ensuring that reconstruction efforts are both scientifically grounded and economically justified.