fire evacuation route, SAR imagery, pathfinding optimization, deep learning, multi-hazard ecosystem resilience, post-fire recovery, Environmental sciences, GE1-350

The acceleration of urbanization and the impact of climate change have led to an increasing frequency and intensity of grassland fires, posing severe challenges to resident safety and ecological protection. Traditional static evacuation route planning methods struggle to adapt in real-time to the dynamic changes in fire conditions during emergency management. To address this issue, this paper proposes a grassland fire evacuation route optimization strategy based on the GreyGNN-MARL model. By integrating Synthetic Aperture Radar (Sentinel-1 SAR) imagery, Graph Neural Networks (GNNs), Grey Wolf Optimization (GWO) algorithms, and Multi-Agent Reinforcement Learning (MARL), the model achieves intelligent planning and real-time adjustment of dynamic evacuation routes in fire scenarios. Experimental results demonstrate that this model significantly outperforms traditional methods in terms of evacuation time, risk avoidance success rate, and path safety, with evacuation time reduced by over 25% and risk avoidance success rate improved by approximately 18%. This model provides technical support for emergency management of grassland fires, helping to enhance evacuation efficiency and ensure safety, which is of great significance for smart cities and ecological protection. Future research will focus on further optimizing the model’s computational efficiency and applicability for broader use in fire emergency management in complex environments.