Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data
Abstract
Identifying regions that have high likelihood for wildfires is a key component of land and forestry management and disaster preparedness. We create a data set by aggregating nearly a decade of remote-sensing data and historical fire records to predict wildfires. This prediction problem is framed as three machine learning tasks. Results are compared and analyzed for four different deep learning models to estimate wildfire likelihood. The results demonstrate that deep learning models can successfully identify areas of high fire likelihood using aggregated data about vegetation, weather, and topography with an AUC of 83%.
Cite
@article{arxiv.2010.07445,
title = {Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data},
author = {Fantine Huot and R. Lily Hu and Matthias Ihme and Qing Wang and John Burge and Tianjian Lu and Jason Hickey and Yi-Fan Chen and John Anderson},
journal= {arXiv preprint arXiv:2010.07445},
year = {2021}
}
Comments
Presented at 34th Conference on Neural Information Processing Systems (NeurIPS 2020), Artificial Intelligence for Humani- tarian Assistance and Disaster Response Workshop, Vancouver, Canada