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

Abstract

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.

Integration of Data and Predictive Models for the Evaluation of Air Quality and Noise in Urban Environments

“…For descriptive statistical analysis, measures of central tendency and dispersion are used, and for exploratory data analysis (EDA), visual techniques such as histograms, boxplots, and scatterplots are applied. Regression algorithms, classifiers such as support vector machines, and neural networks are used to predict pollution levels in predictive modeling [ 42 , 43 ]. Clustering algorithms identify patterns in unlabeled data, such as k-means or Density-Based Spatial Clustering of Applications with Noise (DBSCAN).…”

An improved K-Means clustering based on differential evolution

“…The main advantages of the algorithm are: simple structure and low time complexity. The disadvantages are: the number of classes k needs to be predetermined; the randomly selected initial center point is easy to fall into the local optimum; isolated data and noise data affect the clustering effect [7][8].…”

Position-Feature Attention Network-Based Approach for Semantic Segmentation of Urban Building Point Clouds from Airborne Array Interferometric SAR

“…Inspired by density clustering, Ziye Guo and others proposed using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to extract various shapes of buildings, preserving the architectural structure more completely [19]. Afterwards, A. Mele et al integrated satellite DInSAR measurements with the DBSCAN algorithm to cluster building areas [20]. Ziye Guo et al introduced Euclidean clustering with KD-Tree for extracting and segmenting individual building facades [21].…”