Hierarchical Clustering and Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR) for Remotely Sensed Building Identification and Risk Prioritisation

Abstract

The conventional Structural Health Monitoring (SHM) framework focuses on individual structures. However, preliminary studies are required at a large territorial scale to effectively identify the most vulnerable elements. This becomes particularly challenging in urban settings, where numerous buildings of varied shapes, ages, and structural conditions are closely spaced from one another. A twofold task is therefore required: the automated identification and differentiation of various structures, coupled with a ranking system based on perceived structural risk, here assumed to be linked to their deformation patterns. It integrates displacement measurements acquired through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique, specifically employing the full-resolution Small Baseline Subset (SBAS) approach coupled with Hierarchical Clustering. The effectiveness of this method is successfully demonstrated and validated in two selected areas of Rome, Italy, serving as case studies. The results of this vast-area scale monitoring can be used to select the constructions that need a more in-depth assessment.

Keywords

• machine learning
• Hierarchical Clustering
• Differential Interferometric Synthetic Aperture Radar (DInSAR)
• Small Baseline Subset (SBAS)
• COSMO-SkyMed
• QGIS