Wildland fire detection and monitoring using a drone-collected RGB/IR image dataset

Abstract

Current forest monitoring technologies including satellite remote sensing, manned/piloted aircraft, and observation towers leave uncertainties about a wildfire's extent, behavior, and conditions in the fire's near environment, particularly during its early growth. Rapid mapping and real-time fire monitoring can inform in-time intervention or management solutions to maximize beneficial fire outcomes. Drone systems' unique features of 3D mobility, low flight altitude, and fast and easy deployment make them a valuable tool for early detection and assessment of wildland fires, especially in remote forests that are not easily accessible by ground vehicles. In addition, the lack of abundant, well-annotated aerial datasets - in part due to unmanned aerial vehicles' (UAVs') flight restrictions during prescribed burns and wildfires - has limited research advances in reliable data-driven fire detection and modeling techniques. While existing wildland fire datasets often include either color or thermal fire images, here we present (1) a multi-modal UAV-collected dataset of dual-feed side-by-side videos including both RGB and thermal images of a prescribed fire in an open canopy pine forest in Northern Arizona and (2) a deep learning-based methodology for detecting fire and smoke pixels at accuracy much higher than the usual single-channel video feeds. The collected images are labeled to ``fire'' or ``no-fire'' frames by two human experts using side-by-side RGB and thermal images to determine the label. To provide context to the main dataset's aerial imagery, the included supplementary dataset provides a georeferenced pre-burn point cloud, an RGB orthomosaic, weather information, a burn plan, and other burn information. By using and expanding on this guide dataset, research can develop new data-driven fire detection, fire segmentation, and fire modeling techniques.

Citation

Chen, Xiwen; Hopkins, Bryce; Wang, Hao; O'Neill, Leo; Afghah, Fatemeh; Razi, Abolfazl; Fule, Peter; Coen, Janice; Rowell, Eric; Watts, Adam. 2022. Wildland fire detection and monitoring using a drone-collected RGB/IR image dataset. IEEE Access. 10: 121301-121317. https://doi.org/10.1109/ACCESS.2022.3222805.