WildfireDB: An Open-Source Dataset Connecting Wildfire Spread with Relevant Determinants

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 <em>WildfireDB</em>, contains over 17 million data points that capture how fires spread in the continental USA in the last decade. The paper accompanying this dataset is part of the 2021 Neural Information Processing Systems (NeurIPS) Dataset and Benchmark Track. The paper describes the algorithmic approach used to create 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. Please see https://colab.research.google.com/drive/1cm2Z4E0HzXMAcuUrE26wHXL2FS_pIj3t?usp=sharing for an introduction about how to load the database using python (pandas).火灾蔓延建模在火灾风险管理中具有关键意义。当前，由于缺乏能够将火灾与气象、植被、地形等相关协变量进行关联的全面数据源，构建数据驱动的火灾蔓延预测模型仍面临诸多挑战。本研究发布了首个将历史火灾数据与上述协变量关联的全面开源数据集，命名为WildfireDB。该数据集包含超过1700万个数据点，覆盖过去十年美国本土范围内的火灾蔓延情况。本数据集配套的论文已被收录于2021届神经信息处理系统大会（NeurIPS）数据集与基准赛道。该论文详细阐述了数据集的构建与整合算法、数据集本身的核心特征，并展示了可用于训练野火蔓延预测模型的数据驱动模型的基准测试结果。如需了解如何通过Python（pandas库）加载该数据库，请访问：https://colab.research.google.com/drive/1cm2Z4E0HzXMAcuUrE26wHXL2FS_pIj3t?usp=sharing