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.

中文翻译：

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使用差分变形图检测具有潜在损坏的建筑物


欧洲地面运动服务 （EGMS） 是系统监测和量化整个欧洲陆地位移的重要组成部分。通过使用 Sentinel-1 全分辨率图像，EGMS 为检测自然和人为地震动现象提供了可靠、一致且每年更新的数据集。虽然 Copernicus 平台允许免费访问包含大量测量点的 EGMS 位移图，但挑战在于找到适当的方法和工具，使这些丰富的信息易于利用，尤其是对于非专家。本研究利用 EGMS 位移图展示了一种新型软件工具的能力，该工具旨在自动识别可能因差异运动而受损的城市结构。这种方法值得注意，因为它可以应用于从局部到国家规模的任何规模的区域，并且虽然专为 EGMS 数据而设计，但它可以应用于任何其他 InSAR 衍生的位移图，无论数据源如何。尽管之前对差异沉降进行了研究，但本研究专注于分析每一栋建筑物，并通过使用与每栋建筑物专门相关的测量点来计算变形的空间梯度。设计的软件工具可快速分析和检测有风险的建筑物，应用分类系统，根据其空间变形梯度的严重程度对其进行分类，以不确定性为主要因素，并生成差异变形图。蒙特卡洛模拟帮助我们估计空间梯度的标准差，确定为 0.05 mm×yr-1×m-1。 该方法在 2017 年至 2021 年巴塞罗那市（西班牙）地区使用 EGMS 数据进行了测试。完整的源代码可在 https://github.com/saeedehshahbazi/detecting-differential-deformation.git 上获得。此外，还使用了 COSMO-SkyMed 数据集来评估和评价其性能。在 EGMS 数据集中检测到的建筑物数量为 149 座，在 COSMO-SkyMed 数据集中为 155 座，在两个数据集中，大约 50% 的建筑物被归类为极低/低。结果证明了该技术的稳健性，因为它使用不同的数据集产生了一致的结果。为了验证差异变形图，进行了实地调查以观察任何明显的结构损伤（例如，裂缝或断裂）。差分变形图可能代表了在城市地区进行综合分析和脆弱性/风险评估的主要信息来源。这种技术的有效性，正如不同数据集的一致结果所证明的那样，强调了它在提高我们对结构脆弱性的理解方面的价值，从而有助于明智的城市管理。