  Recent wildfires in the United States have resulted in loss of life and
billions of dollars, destroying countless structures and forests. Fighting
wildfires is extremely complex. It is difficult to observe the true state of
fires due to smoke and risk associated with ground surveillance. There are
limited resources to be deployed over a massive area and the spread of the fire
is challenging to predict. This paper proposes a decision-theoretic approach to
combat wildfires. We model the resource allocation problem as a
partially-observable Markov decision process. We also present a data-driven
model that lets us simulate how fires spread as a function of relevant
covariates. A major problem in using data-driven models to combat wildfires is
the lack of comprehensive data sources that relate fires with relevant
covariates. We present an algorithmic approach based on large-scale raster and
vector analysis that can be used to create such a dataset. Our data with over 2
million data points is the first open-source dataset that combines existing
fire databases with covariates extracted from satellite imagery. Through
experiments using real-world wildfire data, we demonstrate that our forecasting
model can accurately model the spread of wildfires. Finally, we use simulations
to demonstrate that our response strategy can significantly reduce response
times compared to baseline methods.
