Multi-Unmanned-Aerial-Vehicle Wildfire Boundary Estimation Using a Semantic Segmentation Neural Network

Abstract

This paper presents a system to command and control a team of fixed-wing unmanned aerial vehicles (UAVs) to sense dynamic wildfire boundaries. UAV team task and trajectory planning strategies enable the team to rapidly find, rally around, and map the wildfire boundaries. A novel boundary estimation algorithm generates two-dimensional concave polygonal estimates of multiple dynamic boundaries given sparse observation data. The algorithm was tested with simulated wildfire scenario binary fire or free-point observations collected by the UAV team. First, all gathered observations are used to classify groups of points into clusters belonging to individual wildfires; then, spatiotemporal information from wildfire observations is encoded as an image with observation age represented as pixel brightness. A neural network performs semantic segmentation on each image and outputs a predicted binary image of the wildfire. This image is decoded back into a point set that feeds into a boundary estimation algorithm (Polylidar) to extract a concave boundary. Benchmarks for planner and boundary estimation times and accuracy comparisons are provided. Our boundary estimation algorithm and supporting multiagent planning strategies were used to win the 2019 U.S. Air Force Research Laboratory’s Swarm and Search Wildfire challenge using the aerospace multiagent simulation environment.