## 1 Introduction  
The need for widespread structural safety checks, dictated by the high vulnerability of the built heritage in different countries of the world, constitutes a stimulus for the research of advanced techniques for structural monitoring in a built environment. In the last decades, the satellite measurements obtained by applying the Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) technique have given a great impulse to detect superficial deformations, also in the field of the structural engineering. The processing of satellite SAR data permits to obtain ground deformation measurements and thus to detect superficial deformations caused by slow-moving displacement phenomena (on the order of some cm/year), of both natural (e.g., subsidence [1, 2], landslides [3, 4, 5, 6, 7, 8],ground consolidation [9]) and anthropic (e.g., excavations [10], raw materials extraction [11]) origin. These data can be also used to evaluate the vulnerability of systems of constructions under different hypotheses [12; 13]. We remark that the DInSAR technique has been widely used for the large-scale analysis of deformation phenomena [14; 15; 16; 17; 18]. More recently, the possibility of its application in the field of structural monitoring and assessment has been exploited [19; 20; 1; 3; 5; 6; 7; 8; 9], thanks to the increasing number of Earth Observation (EO) satellites in the orbit and to the high frequency of their data sampling. Note that, compared to the traditional monitoring techniques, satellite DInSAR allows covering much larger areas over a longer time span and without the need to install ground instrumentation, resulting in limited costs. Moreover, the possibility to carry out retrospective analyses to investigate the deformative evolution of an area of interest through the generation of time series, by accessing to satellite data acquired down to 1992, enriches the potentialities of this methodology. The management and elaboration of satellite data require the use of advanced and effective processing tools, because of their huge volumes and dynamism, continuously increasing in time. In this scenario, machine learning techniques can be implemented for the extraction of information related to the temporal evolution of the measurement points, for their clustering and the generation of the corresponding deformation maps. It is worth noting that the worldwide literature regarding the combined use of satellite SAR data and artificial intelligence (AI) techniques, aimed at structural monitoring, is very scarce. Despite this, the intuition of applying machine learning techniques is supported by successful applications in the research field of the automatic extraction of building footprints in urban areas from point clouds. Indeed, [PERSON] et al. [21] presented an algorithm for the extraction of building footprints from LIDAR measurements, focusing more on two-dimensional (2-D) footprint extraction than 3-D building models. Then, the majority of the existing approaches derives from largescale airborne laser scanning point clouds. [PERSON] et al. [22] extracted the urban objects from a digital surface model created from aerial laser scanning data, with a fully automatic approach that takes raw 3D points and converts them into sets of clusters through Density-based Spatial Clustering of Applications with Noise (DBSCAN), where each cluster represents an object, such as a building or a portion of a ground surface. [PERSON] and [PERSON] [23] proposed a deep learning-based classification algorithm that integrates different neural networks to classify the point clouds into categories of interest, including trees, buildings, and ground. More recent works propose automatic feature extraction methods using remote-sensing-derived products. [PERSON] et al. [24] proposed a point cloud extraction method based on the DBSCAN algorithm, using Tomographic Synthetic Aperture Radar (TomoSAR) point clouds. Finally, [PERSON] et al.