Bridges are critical components of transportation and communication networks. However, many of these structures are aging and susceptible to damage, underscoring the need for consistent monitoring to ensure their safety. Limited economic and technical resources highlight the importance of adopting smart monitoring strategies to prioritize infrastructure for detailed inspections, evaluations, and interventions. In recent decades, satellite-based remote sensing has gained prominence as a non-invasive tool for large-scale structural monitoring, with particular reference to Multi Temporal Differential SAR Interferometry (MT-DInSAR) . This technology enables the collection of extensive data on the temporal and spatial progression of ground displacements, capturing large-scale deformation phenomena such as subsidence, landslides, and settlements. On a more localized scale, such as for individual bridges, high-resolution and frequent data sampling can be useful for preliminary structural assessments of roads, railways, or specific bridges. This study presents a large-scale methodology for the preliminary structural assessment of bridge networks using satellite-derived deformation data. The approach is applied to three different Proof of Concepts developed within the RETURN extended partnership, utilizing MT-DInSAR measurements from both ascending and descending orbits. By analyzing displacement trends of the measure points, the methodology proposes a classification of the bridegs. This classification can help stakeholders identify the most vulnerable bridges and develop more targeted monitoring and maintenance strategies.