  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 a recall of 84%. Future research will
expand the technique for free burning broadcast fire using thermal images.
