Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset
Wildland Fire Detection and Monitoring Using a Drone-Collected RGB/IR Image Dataset
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DOI：
10.1109/access.2022.3222805
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Xiwen Chen;Bryce Hopkins;Hao Wang;Leo O’Neill;Fatemeh Afghah;A. Razi;Peter Fulé;Janice Coen;Eric Rowell;Adam Watts
中科院分区：
文献类型：
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作者：
Xiwen Chen;Bryce Hopkins;Hao Wang;Leo O’Neill;Fatemeh Afghah;A. Razi;Peter Fulé;Janice Coen;Eric Rowell;Adam Watts
当前的森林监测技术，包括卫星遥感、有人驾驶飞机和瞭望塔，在野火的范围、行为以及火灾近环境状况方面存在不确定性，尤其是在火灾初期增长阶段。快速绘图和实时火灾监测能够为及时干预或管理解决方案提供信息，以最大限度地实现有益的火灾结果。无人机系统具有三维机动性、低空飞行以及快速便捷部署等独特特性，使其成为早期发现和评估野火的宝贵工具，特别是在地面车辆难以到达的偏远森林地区。此外，由于在规定火烧和野火期间无人驾驶飞行器（UAV）的飞行限制等原因，缺乏大量标注良好的航空数据集，这限制了可靠的数据驱动火灾探测和建模技术的研究进展。虽然现有的野火数据集通常要么包含彩色火灾图像，要么包含热成像火灾图像，但在此我们提出：（1）一个由无人机收集的多模态数据集，其中包含在亚利桑那州北部一片开阔松林进行规定火烧时的双馈并排视频，包括RGB图像和热成像图像；（2）一种基于深度学习的方法，用于检测火灾和烟雾像素，其准确率远高于通常的单通道视频数据。收集到的图像由两名人类专家使用并排的RGB图像和热成像图像标记为“有火”或“无火”帧来确定标签。为了给主数据集的航空影像提供背景信息，所包含的补充数据集提供了一个地理参考的火烧前点云、一幅RGB正射镶嵌图、天气信息、一份火烧计划以及其他火烧信息。通过使用和扩展这个引导数据集，研究能够开发新的数据驱动火灾探测、火灾分割和火灾建模技术。
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.