CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia
This work addresses landslide risk assessment for vulnerable mountainous regions, offering a scalable solution for climate-resilient disaster preparedness.
The paper tackles the problem of timely landslide forecasting in high mountain Asia by introducing CC-GRMAS, a multi-agent graph neural system that uses satellite and environmental data to enhance accuracy, enabling real-time situational awareness and proactive disaster response.
Landslides are a growing climate induced hazard with severe environmental and human consequences, particularly in high mountain Asia. Despite increasing access to satellite and temporal datasets, timely detection and disaster response remain underdeveloped and fragmented. This work introduces CC-GRMAS, a framework leveraging a series of satellite observations and environmental signals to enhance the accuracy of landslide forecasting. The system is structured around three interlinked agents Prediction, Planning, and Execution, which collaboratively enable real time situational awareness, response planning, and intervention. By incorporating local environmental factors and operationalizing multi agent coordination, this approach offers a scalable and proactive solution for climate resilient disaster preparedness across vulnerable mountainous terrains.