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

arXiv: https://arxiv.org/abs/1910.06407 · HTML: https://mlsanity.com/paper/1910.06407/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [Incidents1M: a large-scale dataset of images with natural disasters, damage, and incidents](https://mlsanity.com/paper/2201.04236/) (2022)
- [Predicting Forest Fire Using Remote Sensing Data And Machine Learning](https://mlsanity.com/paper/2101.01975/) (2021)

## Abstract (arXiv)

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).