WildfireDB: An Open-Source Dataset Connecting Wildfire Occurrence with Relevant Determinants
Samriddhi Singla, Ayan Mukhopadhyay, Michael Wilbur, Tina Diao, Vinayak Gajjewar, Ahmed Eldawy, Mykel Kochenderfer, Ross D Shachter
Abstract
Modeling fire spread is critical in fire risk management. Creating data-driven models to forecast spread remains challenging due to the lack of comprehensive data sources that relate fires with relevant covariates. We present the first comprehensive and open-source dataset that relates historical fire data with relevant covariates such as weather, vegetation, and topography. Our dataset, named WildfireDB, contains over 17 million data points that capture how fires spread in continental USA in the last decade. In this paper, we describe the algorithmic approach used to process and integrate the data, describe the dataset, and present benchmark results regarding data-driven models that can be learned to forecast the spread of wildfires.
BibTeX
@inproceedings{
singla2021wildfiredb,
title={Wildfire{DB}: An Open-Source Dataset Connecting Wildfire Occurrence with Relevant Determinants},
author={Samriddhi Singla and Ayan Mukhopadhyay and Michael Wilbur and Tina Diao and Vinayak Gajjewar and Ahmed Eldawy and Mykel Kochenderfer and Ross D Shachter and Abhishek Dubey},
booktitle={Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2)},
year={2021},
url={https://openreview.net/forum?id=6nblryHxVbO}
}