FLAME 2: Fire detection and modeLing: Aerial Multi-spectral imagE dataset

Citation Author(s):
Bryce
Hopkins
Clemson University
Leo
O'Neill
Northern Arizona University
Fatemeh
Afghah
Clemson University
Abolfazl
Razi
Clemson University
Eric
Rowell
Desert Research Institute
Adam
Watts
USDA Forest Services
Peter
Fule
Northern Arizona University
Janice
Coen
National Center for Atmospheric Research
Submitted by:
IS-WiN Lab
Last updated:
Thu, 02/16/2023 - 12:02
DOI:
10.21227/swyw-6j78
Data Format:
License:
5
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Abstract 

Drone based wildfire detection and modeling methods enable high-precision, real-time fire monitoring that is not provided by traditional remote fire monitoring systems, such as satellite imaging. Precise, real-time information enables rapid, effective wildfire intervention and management strategies. Drone systems’ ease of deployment, omnidirectional maneuverability, and robust sensing capabilities make them effective tools for early wildfire detection and evaluation, particularly so in environments that are inconvenient for humans and/or terrestrial vehicles. Development of emerging drone-based fire monitoring systems has been inhibited by a lack of well-annotated, high quality aerial wildfire datasets, largely as a result of UAV flight regulations for prescribed burns and wildfires. The included dataset provides a collection of side-by-side infrared and visible spectrum video pairs taken by drones during an open canopy prescribed fire in Northern Arizona in 2021. The frames have been classified by two independent classifiers with two binary classifications. The Fire label is applied when the classifiers visually observe indications of fire in either RGB or IR frame for each frame pair. The Smoke label is applied when the classifiers visually estimate that at least 50% of the RGB frame is filled with smoke. To provide additional context to the main dataset’s aerial imagery, the provided supplementary dataset includes weather information, the prescribed burn plan, a geo-referenced RGB point cloud of the preburn area, an RGB orthomosaic of the preburn area, and links to further information.

Funding Agency: 
This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-20-1-0090 and the National Science Foundation under Grant Numbers CNS-2232048, CNS-2204445, CNS-2038741 and CNS-2038759.

Comments

niece

Submitted by jie Guo on Tue, 11/29/2022 - 01:14

Is this dataset collected with help from the FASMEE project?

Submitted by Amanda Who on Mon, 12/05/2022 - 22:58

Is this dataset collected with help from the FASMEE project?

Submitted by Amanda Who on Mon, 12/05/2022 - 22:58