Optimized Deployment of Unmanned Aerial Vehicles for Wildfire Detection and Monitoring
This work addresses the lack of real-time frontline information for firefighters during wildfires, though it appears incremental as it builds on existing drone-based detection methods by focusing on deployment optimization.
The authors tackled the problem of optimizing drone deployment for wildfire detection and monitoring by proposing a system that uses plane geometry and inequality constrained nonlinear programming to allocate drones for full coverage of wildfire areas, demonstrating its potential with data from eastern Victoria, Australia.
In recent years, increased wildfires have caused irreversible damage to forest resources worldwide, threatening wildlives and human living conditions. The lack of accurate frontline information in real-time can pose great risks to firefighters. Though a plethora of machine learning algorithms have been developed to detect wildfires using aerial images and videos captured by drones, there is a lack of methods corresponding to drone deployment. We propose a wildfire rapid response system that optimizes the number and relative positions of drones to achieve full coverage of the whole wildfire area. Trained on the data from historical wildfire events, our model evaluates the possibility of wildfires at different scales and accordingly allocates the resources. It adopts plane geometry to deploy drones while balancing the capability and safety with inequality constrained nonlinear programming. The method can flexibly adapt to different terrains and the dynamic extension of the wildfire area. Lastly, the operation cost under extreme wildfire circumstances can be assessed upon the completion of the deployment. We applied our model to the wildfire data collected from eastern Victoria, Australia, and demonstrated its great potential in the real world.