The growing need for advanced research in the proactive management of civil engineering works has sparked increasing interest in the integration of geospatial technologies, information modeling, and virtual environments. In this context, satellite radar interferometry (InSAR) has proven to be a reliable tool for multi-Temporal surface displacement monitoring, which can then be used to infer patterns which can subsequently be analyzed to infer patterns of structural or ground deformation. However, the interpretability of InSAR processing is often hindered by two primary challenges: The spatial and directional relativity of the measurements, which are confined to the satellite's line-of-sight and are relative to a userdefined reference point; and the difficulty in precisely geolocating persistent scatterer (PS) points and semantically linking them to specific structural elements, a limitation that stems from undetermined geolocation precision and the lack of inherent contextual information in the InSAR data itself. This research introduces an integrated methodological approach combining high-resolution satellite InSAR observations, georeferenced Building Information Models (BIM) to enhance both spatial and semantic accuracy in PS analysis. A key element is the structured data integration between heterogeneous sources such as satellite observations, geospatial coordinates, and BIM geometry, enabled by the geodetic alignment of InSAR data with absolute terrestrial reference systems and the projection of PS within the BIM environment. This process enables more reliable association of PS with discrete construction elements. This semantic mapping, combined with three-dimensional representation, allows for a more comprehensive interpretation of detected displacements, supporting the identification of potential issues not directly linked to structural failures. The resultant BIM serves as the connection between infrastructure elements and the processed InSAR displacement estimates, thus improving the reliability of the analysis as well as promoting a push towards operational deployment for a Digital Twin system. The application in case studies demonstrates the potential of the dynamic and multimodal Digital Twin paradigm as an operational tool for decision support, predictive maintenance, and infrastructure resilience.