Landslide research benefits from a multidisciplinary approach that integrates observations of slope morphology, surface conditions, and their evolution over time. The growing availability of different sensor types and long-term datasets, along with recent advances in remote sensing techniques and data-driven methods, offers great opportunities to investigate landslides. This session brings together recent developments in landslide identification and characterization and particularly welcomes contributions including:
• Automatic and semi-automatic landslide detection and classification, including machine- and deep-learning approaches;
• Regional-scale landslide inventories, landslide susceptibility assessment, monitoring and early-warning applications;
• Integration of heterogeneous datasets from different techniques (optical, SAR, InSAR time series, DEM-derived, and geophysical information) for landslide identification and characterization;
• Application of geomorphological mapping, repeated topographic observations, long-term monitoring, and multi-temporal DEMs (e.g., bistatic SAR/TanDEM-X);
We welcome contributions addressing large landslides across different triggering mechanisms, scales, and environmental settings, from local case studies to regional-scale applications.