## 5 Conclusions  
The spread of the advanced DInSAR techniques for the generation of spatially dense deformation time series has highly contributed to the development of several applications, among which we underline those relevant to the structural stability assessment of buildings and infrastructures. On the other hand, within this framework, the large data volumes of the generated DInSAR products need to be properly accounted, when dealing with automatic tools for the extraction of relevant information. In this context, a strong support may derive from the exploitation of the AI techniques, which represent powerful instruments for automatically extracting significant information associated to features. In this work, a methodology, jointly exploiting satellite DInSAR measurements and DBSCAN-based techniques, has been proposed for the preliminary identification and ranking of possible critical constructions in a built-up environment. This methodology has been successfully applied to CSK-based DInSAR time series relevant to three sample areas within the city of Rome (Italy). In particular, the DBSCAN clustering algorithm has allowed the automatic extraction of urban buildings from PSs clouds. Then, for each cluster, an elaboration of the data has led to obtain the picture of the deformation condition in the investigated period, for each considered area. Indeed, by studying the velocity trends and statistics of the PSs belonging to the cluster-identified buildings, synthetic deformation maps of the investigated area (with focus on the buildings) are retrieved, which allow to carry out a preliminary identification and ranking of critical buildings to be further investigated. Some proposals for future developments aimed to improve the presented methodology are highlighted. The hyper-parameters of the DBSCAN will be refined, to detect the one-floor buildings (that in this work have been neglected). Otherwise, further different clustering algorithms, e.g., OPTICS [52], could be integrated in our methodology. Moreover,fitting methods could be implemented to fit the model through the original SAR images, in order to extract information on buildings by overcoming the clustering step and refine their identification. The clustering results could also be improved, to avoid the inclusion of noise clusters that are not representative of a building but are referred to other elements that reflect the signal emitted by the satellite. This could be accomplished by imposing an additional condition: the clusters which occupy an area lower than a minimum value (assuming a minimum dimensions of a building) could be classified as noise. Finally, the temporal coherence of the PSs can be considered as a driven parameter in two different ways: (a) a pre-selection on the PSs dataset could be done, considering only the PSs characterized by values of temporal coherence greater than a certain threshold (e.g., 0.6-0.7), to increase the reliability of the results; (b) a post-processing on the statistics of the velocity values can be implemented by weighting the single value for their relative temporal coherence. The presented approach could give a decisive impulse to the automation of the preliminary structural monitoring of a built environment at a large spatial scale, beyond the specific structural typologies. In particular, the resulting maps could provide support in decisional phases, as well as to the organization of preventive measures to reduce the risks for the built environment associated to the measured deformations. Therefore, the possibility to automatize the imposition of trigger alarms for the areas under investigation through the joint exploitation of satellite and AI techniques could represent a very relevant prevention tool. The code developed to implement the methodology is available under request, by contacting the authors. Conceptualization, R.L., G.A. and A.P. ; formal analysis, A.M. ([PERSON]), A.V., M.B. and A.M. ([PERSON]); methodology, A.M. ([PERSON]), A.V., M.B. and A.M. ([PERSON]); supervision, R.L., G.A. and A.P.