Enhancing Wildfire Propagation Estimation with an Autonomous Aerial Agent

Accurately estimating wildfire propagation is pivotal for effective firefighting strategies and informed decision-making. This paper introduces a novel solution that harnesses an autonomous uncrewed aerial vehicle (UAV) agent with advanced sensing capabilities in conjunction with a wildfire spread model and the Ensemble Kalman Filter (EnKF) to enhance wildfire propagation estimations. By strategically guiding the UAV to specific areas ahead of the fire, it collects real-time data on fuel types and weather conditions, which are fused with available historical data to improve accuracy. The updated data are then utilized by the wildfire spread model and EnKF to provide estimations of wildfire propagation, which further improves the UAV path planning. The performance of the proposed solution is demonstrated in a simulated environment, highlighting its ability to enhance wildfire propagation estimation and improve the fire’s current perimeter state estimation.