Detection of buildings with potential damage using differential deformation maps

The European Ground Motion Service (EGMS) is a crucial component of the systematic monitoring and quantification of land displacement across Europe. By using Sentinel-1 full-resolution images, EGMS offers a reliable, consistent, and annually updated dataset for detecting natural and anthropogenic ground motion phenomena. While the Copernicus platform grants free accessibility to EGMS displacement maps containing a massive number of measurement points, the challenge lies in finding appropriate methodologies and tools to make this wealth of information easily exploitable, especially to non-experts. This study leverages the EGMS displacement maps to display the capability of a novel software tool designed for the automatic identification of urban structures that may be susceptible to damage due to differential movements. This approach is notable since it can be applied to areas of any size, from local to national scale, and while designed for EGMS data, it can be applied to any other InSAR-derived displacement maps, regardless of the data source. Despite prior researches on differential settlements, this study concentrates on analysing every single buildings and computing the spatial gradient of deformation by using Measurement Points specifically associated with each building. The designed software tool quickly analyses and detects at-risk buildings, applying a classification system that categorizes them based on the severity of their spatial deformation gradient, with uncertainty as the primary factor, and generates a differential deformation map as a result. The Monte Carlo simulations assisted us in estimating the standard deviation of the spatial gradient, which was determined to be 0.05 mm×yr −1 ×m −1 . The approach was tested with EGMS data over the area of Barcelona Municipality (Spain) from 2017 to 2021. The complete source code is available at https://github.com/saeedehshahbazi/detecting-differential-deformation.git . In addition, a COSMO-SkyMed dataset was used to assess and appraise its performance. The number of detected buildings was 149 in the EGMS dataset and 155 in the COSMO-SkyMed dataset, with approximately 50% of the buildings classified as Very-Low/Low in both datasets. The results demonstrate the technique’s robustness, as it yielded consistent outcomes using distinct datasets. To verify the differential deformation map, a field survey was conducted to observe any evident structural damage (e.g., cracks or fractures). The differential deformation maps may represent a primary source of information for conducting a comprehensive analysis and vulnerability/risk assessment in urban areas. The effectiveness of this technique, as evidenced by consistent outcomes with diverse datasets, underlines its value in improving our understanding of structural vulnerabilities, thus contributing to informed urban management.