Ron Sarafian

Highlights: What are the main findings? A UNet-based Deep Learning architecture was used to detect subsidence patterns observed in Interferometric Synthetic Aperture Radar (InSAR) measurements. Different train–test partition schemes based on random patches, temporal division, and geographic distribution showed high inference performance, with object-level metric scores above 0.8. What are the implications of the main findings? The UNet architecture shows strong potential for automating the delineation of sinkhole-induced subsidence from individual wrapped interferograms, reducing post-processing overhead and human errors, and enhancing the efficiency and reliability of sinkhole activity monitoring. Different train–test partitioning schemes reveal a clear hierarchy of model generalization, quantified using object-level performance metrics, from recognizing partially seen subsidence patterns to transferring across unseen acquisitions and geospatial regions. The Dead Sea (DS) region has experienced a sharp increase in sinkhole formation in recent years, posing environmental and infrastructure risks. The Geological Survey of Israel (GSI) employs Interferometric Synthetic Aperture Radar (InSAR) to monitor sinkhole activity and manually map land subsidence along the western shore of the DS. This process is both time-consuming and prone to human error. Automating detection with Deep Learning (DL) offers a transformative opportunity to enhance monitoring precision, scalability, and real-time decision-making. DL segmentation architectures such as UNet, Attention UNet, SAM, TransUNet, and SegFormer have shown effectiveness in learning geospatial deformation patterns in InSAR and related remote sensing data. This study provides a first comprehensive evaluation of a DL segmentation model applied to InSAR data for detecting land subsidence areas that occur as part of the sinkhole-formation process along the western shores of the DS. Unlike image-based tasks, our new model learns interferometric phase patterns that capture subtle ground deformations rather than direct visual features. As the ground truth in the supervised learning process, we use subsidence areas delineated on the phase maps by the GSI team over the years as part of the operational subsidence surveillance and monitoring activities. This unique data poses challenges for annotation, learning, and interpretability, making the dataset both non-trivial and valuable for advancing research in applied remote sensing and its application in the DS. We train the model across three partition schemes, each representing a different type and level of generalization, and introduce object-level metrics to assess its detection ability. Our results show that the model effectively identifies and generalizes subsidence areas in InSAR data across different setups and temporal conditions and shows promising potential for geographical generalization in previously unseen areas. Finally, large-scale subsidence trends are inferred by reconstructing smaller-scale patches and evaluated for different confidence thresholds.