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We present the Wildland-fire Infrared Thermal (WIT-UAS) dataset for long-waveinfrared sensing of crew and vehicle assets amidst prescribed wildland fireenvironments. While such a dataset is crucial for safety monitoring in wildlandfire applications, to the authors' awareness, no such dataset focusing onassets near fire is publicly available. Presumably, this is due to the barrierto entry of collaborating with fire management personnel. We present tworelated data subsets: WIT-UAS-ROS consists of full ROS bag files containingsensor and robot data of UAS flight over the fire, and WIT-UAS-Image containshand-labeled long-wave infrared (LWIR) images extracted from WIT-UAS-ROS. Ourdataset is the first to focus on asset detection in a wildland fireenvironment. We show that thermal detection models trained without fire datafrequently detect false positives by classifying fire as people. By adding ourdataset to training, we show that the false positive rate is reducedsignificantly. Yet asset detection in wildland fire environments is stillsignificantly more challenging than detection in urban environments, due todense obscuring trees, greater heat variation, and overbearing thermal signalof the fire. We publicize this dataset to encourage the community to study moreadvanced models to tackle this challenging environment. The dataset, code andpretrained models are available at\url{https://github.com/castacks/WIT-UAS-Dataset}.

WIT-UAS: A Wildland-fire Infrared Thermal Dataset to Detect Crew Assets From Aerial Views