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).
Cite
@article{arxiv.1910.06407,
title = {FireNet: Real-time Segmentation of Fire Perimeter from Aerial Video},
author = {Jigar Doshi and Dominic Garcia and Cliff Massey and Pablo Llueca and Nicolas Borensztein and Michael Baird and Matthew Cook and Devaki Raj},
journal= {arXiv preprint arXiv:1910.06407},
year = {2019}
}
Comments
Published at NeurIPS 2019; Workshop on Artificial Intelligence for Humanitarian Assistance and Disaster Response(AI+HADR 2019)