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