CC-GRMAS: A Multi-Agent Graph Neural System for Spatiotemporal Landslide Risk Assessment in High Mountain Asia

TL;DR

CC-GRMAS is a multi-agent graph neural system that integrates satellite data and environmental signals to improve real-time landslide risk forecasting and disaster response in high mountain Asia.

Contribution

The paper introduces CC-GRMAS, a novel multi-agent framework that combines satellite observations and environmental data for enhanced landslide prediction and management.

Findings

Improved accuracy in landslide risk forecasting.

Effective real-time situational awareness and response planning.

Scalable multi-agent coordination in mountainous terrains.

Abstract

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.