Landslides and slope instabilities represent significant global hazards, causing substantial damage and loss of life annually. Despite this impact, the fundamental triggering mechanisms remain a key area of ongoing research. Landslide-prone areas and slope instabilities are characterized by complex, heterogeneous subsurface properties and dynamic processes operating across a wide range of timescales – from seconds to decades – and spatial scales – from grain size to slope dimensions. Effectively identifying and predicting instability processes and ultimately failure requires innovative approaches that account for these wide temporal and spatial variabilities.
This session seeks contributions presenting novel methods,emerging trends, and case studies in landslide and slope instability reconnaissance, monitoring, and early warning. We particularly encourage submissions showcasing the integration of geophysical, geotechnical, geological, and remote sensing data to build a landslide model able to characterize the landslide architecture and track its evolution.
We especially invite abstracts demonstrating:
• Multi-method approaches combining geophysical, geotechnical, and remote sensing techniques.
• Applications of machine learning to landslide hazard assessment and prediction.
• Time-lapse geophysical surveys for monitoring subsurface changes.
• Determination of geomechanical parameters through integrated geological (e.g., borehole data, geotechnical surveys) and geophysical studies.
Recognizing the cross-disciplinary nature of this challenge, we welcome contributions addressing a broad range of slope instability types, including avalanches, natural and engineered slopes, and climate-induced failures.