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In this paper, we share our approach to real-time segmentation of fireperimeter from aerial full-motion infrared video. We start by describing theproblem from a humanitarian aid and disaster response perspective.Specifically, we explain the importance of the problem, how it is currentlyresolved, and how our machine learning approach improves it. To test our modelswe annotate a large-scale dataset of 400,000 frames with guidance from domainexperts. Finally, we share our approach currently deployed in production withinference speed of 20 frames per second and an accuracy of 92 (F1 Score).

FireNet: Real-time Segmentation of Fire Perimeter from Aerial Video