Landslides pose significant threats to life and property, particularly in mountainous regions. To address this, this study develops a landslide susceptibility model integrating Earth Observation (EO) data, historical data, and Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) ground movement results. The model categorizes areas into four susceptibility classes (from Class 1 to Class 4) using a multi-class classification approach. Results indicate that the Xtreme Gradient Boosting (XGB) model effectively predicts landslide susceptibility with area under the curve (AUC) values ranging from 0.93 to 0.97, with high accuracy of 0.89 and a balanced performance across different susceptibility classes. The integration of MT-InSAR data enhances the model’s ability to capture dynamic ground movement and improves landslide mapping. The landslide susceptibility map generated by the XGB model indicates high susceptibility along the Pacific coast. The optimal model was validated against 272 historical landslide occurrences, with predictions distributed as follows: 68 occurrences (25%) in Class 1, 142 occurrences (52%) in Class 2, 58 occurrences (21.5%) in Class 3, and 4 occurrences (1.5%) in Class 4. This study highlights the importance of considering temporal changes in environmental conditions such as precipitation, distance to streams, and changes in vegetation for accurate landslide susceptibility assessment.

Remote sensing techniques are invaluable for identifying potential mineralisation zones, monitoring active mining operations, and assessing safety conditions around closed and abandoned mines. This study focuses on the Amiantos open pit asbestos mine in Cyprus, which was operational from the early 20th century until 1988, with the mining lease formally concluding in 1992. Despite rehabilitation efforts initiated by the Cypriot government post-closure, abandoned mines continue to pose risks such as ground deformation, slope instability, and groundwater contamination. The primary objective of this research is to assess ground stability and analyse both vertical and horizontal ground displacement in the Amiantos mine area using multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques. MT-InSAR enables millimetre-scale detection of surface deformation, providing crucial insights into post-mining land stability and associated hazards. The study also evaluates potential risks to nearby local communities, particularly the Kato Amiantos village. The analysis is based on Sentinel-1A C-band SAR data acquired from January 2020 to December 2024. Using the small baseline subset (SBAS) MT-InSAR approach, significant ground movements were detected, primarily in the western portion of the mine area with some points exhibiting horizontal (eastward) displacements up to 200 mm over the five-year period. In many locations, horizontal displacements are more pronounced than the vertical, suggesting subsurface instability potentially linked to backfilled material from earlier mining activities. The findings highlight the critical need for continuous, long-term monitoring using satellite-based observations, even after mining activities have ceased. The results underscore the importance of remote sensing in guiding risk mitigation strategies for nearby communities and infrastructure.