  Predicting wildfire spread is critical for land management and disaster
preparedness. To this end, we present `Next Day Wildfire Spread,' a curated,
large-scale, multivariate data set of historical wildfires aggregating nearly a
decade of remote-sensing data across the United States. In contrast to existing
fire data sets based on Earth observation satellites, our data set combines 2D
fire data with multiple explanatory variables (e.g., topography, vegetation,
weather, drought index, population density) aligned over 2D regions, providing
a feature-rich data set for machine learning. To demonstrate the usefulness of
this data set, we implement a neural network that takes advantage of the
spatial information of this data to predict wildfire spread. We compare the
performance of the neural network with other machine learning models: logistic
regression and random forest. This data set can be used as a benchmark for
developing wildfire propagation models based on remote sensing data for a lead
time of one day.
