Wildfires are one of the costliest and deadliest natural disasters in the US, causing damage to millions of hectares of forest resources and threatening the lives of people and animals. Of particular importance are risks to firefighters and operational forces, which highlights the need for leveraging technology to minimize danger to people and property. FLAME (Fire Luminosity Airborne-based Machine learning Evaluation) offers a dataset of aerial images of fires along with methods for fire detection and segmentation which can help firefighters and researchers to develop optimal fire management strategies.

This paper provides a fire image dataset collected by drones during a prescribed burning piled detritus in an Arizona pine forest. The dataset includes video recordings and thermal heatmaps captured by infrared cameras. The captured videos and images are annotated, and labeled frame-wise to help researchers easily apply their fire detection and modeling algorithms. The paper also highlights solutions to two machine learning problems: (1) Binary classification of video frames based on the presence [and absence] of fire flames. An Artificial Neural Network (ANN) method is developed that achieved a 76% classification accuracy. (2) Fire detection using segmentation methods to precisely determine fire borders. A deep learning method is designed based on the U-Net up-sampling and down-sampling approach to extract a fire mask from the video frames. Our FLAME method approached a precision of 92%, and recall of 84%. Future research will expand the technique for free burning broadcast fire using thermal images.

野火是美国最昂贵和最致命的自然灾害之一，对数百万公顷的森林资源造成破坏，并威胁着人类和动物的生命。消防员和作战部队面临的风险尤为重要，这凸显了需要利用技术来最大程度地减少对人员和财产的危害。FLAME（基于火光的机载机器学习评估）提供了火灾航空图像的数据集以及火灾检测和分割的方法，可以帮助消防员和研究人员制定最佳的火管理策略。

本文提供了在亚利桑那州的一片松树林中，在规定的燃烧堆积碎屑期间，无人机收集的火场图像数据集。数据集包括红外摄像机捕获的视频记录和热图。对捕获的视频和图像进行注释，并按帧进行标记，以帮助研究人员轻松地应用其火灾探测和建模算法。本文还着重介绍了两个机器学习问题的解决方案：（1）基于火焰的存在和不存在对视频帧进行二进制分类。开发了一种人工神经网络（ANN）方法，该方法实现了76％的分类精度。（2）使用分段方法进行火灾探测，以精确确定火灾边界。基于U-Net上采样和下采样方法设计了一种深度学习方法，以从视频帧中提取防火面具。我们的FLAME方法的精确度达到了92％，召回率达到了84％。未来的研究将扩展使用热图像自由燃烧广播火的技术。