Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset
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...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9953997/ |
| _version_ | 1797986791537508352 |
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| author | Xiwen Chen Bryce Hopkins Hao Wang Leo O'Neill Fatemeh Afghah Abolfazl Razi Peter Fule Janice Coen Eric Rowell Adam Watts |
| author_facet | Xiwen Chen Bryce Hopkins Hao Wang Leo O'Neill Fatemeh Afghah Abolfazl Razi Peter Fule Janice Coen Eric Rowell Adam Watts |
| author_sort | Xiwen Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T07:38:38Z |
| format | Article |
| id | doaj.art-930193290d6446ec8f295424a3b8ef33 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T07:38:38Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-930193290d6446ec8f295424a3b8ef332022-12-22T04:36:39ZengIEEEIEEE Access2169-35362022-01-011012130112131710.1109/ACCESS.2022.32228059953997Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image DatasetXiwen Chen0https://orcid.org/0000-0002-8006-4383Bryce Hopkins1Hao Wang2https://orcid.org/0000-0002-3035-3064Leo O'Neill3https://orcid.org/0000-0001-5734-0979Fatemeh Afghah4https://orcid.org/0000-0002-2315-1173Abolfazl Razi5https://orcid.org/0000-0002-3330-6132Peter Fule6Janice Coen7https://orcid.org/0000-0003-3927-989XEric Rowell8Adam Watts9School of Computing, Clemson University, Clemson, SC, USADepartment of Electrical and Computer Engineering, Clemson University, Clemson, SC, USASchool of Computing, Clemson University, Clemson, SC, USASchool of Forestry, Northern Arizona University, Flagstaff, AZ, USADepartment of Electrical and Computer Engineering, Clemson University, Clemson, SC, USASchool of Computing, Clemson University, Clemson, SC, USASchool of Forestry, Northern Arizona University, Flagstaff, AZ, USANational Center for Atmospheric Research, Boulder, CO, USADesert Research Institute, Reno, NV, USAU.S. Forest Service, Pacific Wildland Fire Science Laboratory, Seattle, WA, USACurrent 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.https://ieeexplore.ieee.org/document/9953997/Data-driven fire detectionprescribed firefire modelingfire dataunmanned aerial vehicle (UAV)deep learning |
| spellingShingle | Xiwen Chen Bryce Hopkins Hao Wang Leo O'Neill Fatemeh Afghah Abolfazl Razi Peter Fule Janice Coen Eric Rowell Adam Watts Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset IEEE Access Data-driven fire detection prescribed fire fire modeling fire data unmanned aerial vehicle (UAV) deep learning |
| title | Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset |
| title_full | Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset |
| title_fullStr | Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset |
| title_full_unstemmed | Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset |
| title_short | Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset |
| title_sort | wildland fire detection and monitoring using a drone collected rgb ir image dataset |
| topic | Data-driven fire detection prescribed fire fire modeling fire data unmanned aerial vehicle (UAV) deep learning |
| url | https://ieeexplore.ieee.org/document/9953997/ |
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