Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset

Xiwen Chen, Bryce Hopkins, Hao Wang, Leo O'Neill, Fatemeh Afghah, Abolfazl Razi, Peter Fulé, Janice Coen, Eric Rowell, Adam Watts

Research output: Contribution to journalArticlepeer-review

42 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)121301-121317
Number of pages17
JournalIEEE Access
StatePublished - 2022


  • Data-driven fire detection
  • deep learning
  • fire data
  • fire modeling
  • prescribed fire
  • unmanned aerial vehicle (UAV)

ASJC Scopus subject areas

  • General Engineering
  • General Computer Science
  • General Materials Science


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