Automatic Wildfire Detection and Simulation using Optical Information from Unmanned Aerial Systems 2015-01-2474
In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.
Citation: Lum, C., Summers, A., Carpenter, B., Rodriguez, A. et al., "Automatic Wildfire Detection and Simulation using Optical Information from Unmanned Aerial Systems," SAE Technical Paper 2015-01-2474, 2015, https://doi.org/10.4271/2015-01-2474. Download Citation
Christopher W. Lum, Alexander Summers, Brian Carpenter, Angel Rodriguez, Matthew Dunbabin
University of Washington, Queensland University of Technology