构建首个覆盖全美五年野火的多任务遥感数据集，支持火点检测、烧毁面积追踪与次日蔓延预测。
TS-SatFire: A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and Prediction
- 整合多时相遥感影像与气象地形等辅助数据，标注火点与烧毁区域
- 涵盖2017–2021年美国本土3552幅影像，总大小71GB，支持三类任务
- 适用于遥感、灾害预测与深度学习研究者，推动野火智能监测发展
野火监测与预测对理解火行为至关重要。借助丰富的地球观测数据，可通过多任务深度学习模型实现集成与提升。本文提出一个综合性多时相遥感数据集，用于主动火点检测、每日野火监测及次日火势预测。数据覆盖2017年1月至2021年10月美国本土野火事件，包含3552幅地表反射率影像及气象、地形、土地覆盖与可燃物等辅助数据，总计71 GB。每场野火生命周期均被记录，标注了主动火点（AF）与烧毁区域（BA），并经人工质量验证。该数据集支持三项任务：a) 主动火点检测；b) 每日烧毁面积制图；c) 野火演变预测。检测任务基于多光谱、多时相图像进行像素级分类，预测任务则融合卫星与辅助数据建模火势动态。该数据集及其基准测试为推进深度学习在野火研究中的应用奠定基础。
原文摘要 · Abstract (English)
Wildfire monitoring and prediction are essential for understanding wildfire behaviour. With extensive Earth observation data, these tasks can be integrated and enhanced through multi-task deep learning models. We present a comprehensive multi-temporal remote sensing dataset for active fire detection, daily wildfire monitoring, and next-day wildfire prediction. Covering wildfire events in the contiguous U.S. from January 2017 to October 2021, the dataset includes 3552 surface reflectance images and auxiliary data such as weather, topography, land cover, and fuel information, totalling 71 GB. The lifecycle of each wildfire is documented, with labels for active fires (AF) and burned areas (BA), supported by manual quality assurance of AF and BA test labels. The dataset supports three tasks: a) active fire detection, b) daily burned area mapping, and c) wildfire progression prediction. Detection tasks use pixel-wise classification of multi-spectral, multi-temporal images, while prediction tasks integrate satellite and auxiliary data to model fire dynamics. This dataset and its benchmarks provide a foundation for advancing wildfire research using deep learning.
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