Beyond the European Ground Motion Service: Identification and Classification of Unstable Areas at the National Level

The recent availability of high-resolution, open-access MT-InSAR data, alongside free tools for data interpretation such asADAtools, has enabled the development of wide-area, value-added geospatial products. This study demonstrates the integration of data delivered by the European Ground Motion Service with open-access ancillary datasets andADAtoolsto efficiently identify, map, and classify ground deformation phenomena across Spain between 2015 and 2021. By processing extensive datasets and clustering measurement points into Active Deformation Areas (ADAs), this approach reduces data complexity and simplifies the interpretation by focusing the analysis and classification on significant deformation areas. A central component of this study is the validation of the ADA classification results. Expert user validation addressed discrepancies and ensured the accuracy of the classifications, particularly in complex scenarios. In addition, theADAclassifieroutput was compared with an independently generated map using a machine learning approach based on the extreme gradient boosting method. The high degree of consistency observed between the two methods reinforces the reliability of theADAclassifier. The classification results highlighted subsidence and landslides as prevalent phenomena, aligning with known geohazard distributions. While theADAclassifiereffectively identified subsidence, landslides, and uplifts, it faced challenges in distinguishing construction settlement and sinkholes, indicating a need for further refinement. Future work should focus on refining decision-tree methodologies, integrating time-series data, and enhancing classification accuracy for overlapping deformation types. This methodology offers a scalable approach for systematic geohazard monitoring that can be adapted to other regions, supporting risk assessment and the development of targeted mitigation strategies.