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

PDF

Related Concepts

Segmentation Perimeter Computer science Perspective (graphical) Inference Artificial intelligence Domain (mathematical analysis) Computer vision Motion (physics) Scale (ratio) Machine learning Cartography Geography Mathematics Geometry Mathematical analysis

Jigar Doshi , Dominic Garcia , Cliff Massey , Pablo Llueca , Nicolas Borensztein , Michael L. Baird , Matthew Cook , Devaki Raj ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.48550/arxiv.1910.06407 · OA: W2981094396

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).