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 dataset of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire datasets based on Earth observation satellites, our dataset combines 2-D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, and population density) aligned over 2-D regions, providing a feature-rich dataset for machine learning. To demonstrate the usefulness of this dataset, we implement a neural network that takes advantage of the spatial information of these data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This dataset can be used as a benchmark for developing wildfire propagation models based on remote-sensing data for a lead time of one day.

中文翻译：

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次日野火蔓延：从遥感数据预测野火蔓延的机器学习数据集

预测野火蔓延对于土地管理和备灾至关重要。为此，我们展示了“次日野火蔓延”，这是一个精心策划的、大规模、多变量的历史野火数据集，汇总了美国近十年的遥感数据。与基于地球观测卫星的现有火灾数据集相比，我们的数据集将二维火灾数据与多个在二维区域上对齐的解释变量（例如，地形、植被、天气、干旱指数和人口密度）相结合，提供了一个特征- 丰富的机器学习数据集。为了证明这个数据集的有用性，我们实现了一个神经网络，利用这些数据的空间信息来预测野火蔓延。我们将神经网络的性能与其他机器学习模型进行比较：逻辑回归和随机森林。该数据集可用作基于遥感数据开发野火传播模型的基准，前置时间为一天。