WIT-UAS: A Wildland-fire Infrared Thermal Dataset to Detect Crew Assets
  From Aerial Views
Andrew Jong Mukai Yu Devansh Dhrafani Siva Kailas Brady Moon Katia Sycara Sebastian Scherer
We present the Wildland-fire Infrared Thermal (WIT-UAS) dataset for long-wave
infrared sensing of crew and vehicle assets amidst prescribed wildland fire
environments. While such a dataset is crucial for safety monitoring in wildland
fire applications, to the authors' awareness, no such dataset focusing on
assets near fire is publicly available. Presumably, this is due to the barrier
to entry of collaborating with fire management personnel. We present two
related data subsets: WIT-UAS-ROS consists of full ROS bag files containing
sensor and robot data of UAS flight over the fire, and WIT-UAS-Image contains
hand-labeled long-wave infrared (LWIR) images extracted from WIT-UAS-ROS. Our
dataset is the first to focus on asset detection in a wildland fire
environment. We show that thermal detection models trained without fire data
frequently detect false positives by classifying fire as people. By adding our
dataset to training, we show that the false positive rate is reduced
significantly. Yet asset detection in wildland fire environments is still
significantly more challenging than detection in urban environments, due to
dense obscuring trees, greater heat variation, and overbearing thermal signal
of the fire. We publicize this dataset to encourage the community to study more
advanced models to tackle this challenging environment. The dataset, code and
pretrained models are available at
\url{https://github.com/castacks/WIT-UAS-Dataset}.