On the Joint Exploitation of Satellite DInSAR Measurements and DBSCAN-Based Techniques for Preliminary Identification and Ranking of Critical Constructions in a Built Environment

The need for widespread structural safety checks represents a stimulus for the research of advanced techniques for structural monitoring at the scale of single constructions or wide areas. In this work, a strategy to preliminarily identify and rank possible critical constructions in a built environment is presented, based on the joint exploitation of satellite radar remote sensing measurements and artificial intelligence (AI) techniques. The satellite measurements are represented by the displacement time series obtained through the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique known as full resolution Small BAseline Subset (SBAS) approach, while the exploited AI technique is represented by the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methodology. The DBSCAN technique is applied to the SBAS-DInSAR products relevant to the achieved Persistent Scatterers (PSs), to identify clusters of pixels corresponding to buildings within the investigated area. The analysis of the deformation evolution of each building cluster is performed in terms of velocity rates and statistics on the DInSAR measurements. Synthetic deformation maps of the areas are then retrieved to identify critical buildings. The proposed methodology is applied to three areas within the city of Rome (Italy), imaged by the COSMO-SkyMed SAR satellite constellation from ascending and descending orbits (in the time interval 2011-2019). Starting from the DInSAR measurements, the DBSCAN algorithm provides the automatic clustering of buildings within the three selected areas. Exploiting the derived deformation maps of each study area, a preliminary identification and ranking of critical buildings is achieved, thus confirming the validity of the proposed approach.

Automated summary

This paper proposes a strategy for the preliminary identification and ranking of critical buildings in a built environment, based on the joint exploitation of satellite radar remote sensing measurements and artificial intelligence techniques. By applying the DBSCAN algorithm to the SBAS-DInSAR products, the automatic clustering of buildings and deformation analysis are achieved, leading to the identification and ranking of critical buildings.