TS-SatFire:一个多任务卫星图像时间序列数据集用于野火检测和预测
Yu Zhao1, Sebastian Gerard1, Yifang Ban2
1KTH Royal Institute of Technology, Stockholm, 11428, Sweden.
Scientific data
|November 19, 2025
概括
一个新的遥感数据集有助于野火研究. 它支持使用多任务深度学习模型进行主动火灾检测,日常监测和预测,以更好地管理野火.
科学领域:
- 地球和环境科学 地球和环境科学
- 计算机科学 计算机科学
背景情况:
- 野火监测和预测对于了解火灾行为和减轻风险至关重要.
- 地球观测数据为增强野火分析提供了巨大的潜力.
- 多任务深度学习模型可以整合各种数据源,以获得更好的野火洞察力.
研究的目的:
- 为野火研究引入一个全面的多时空遥感数据集.
- 支持三个关键任务:积极的火灾检测,每日烧毁区域的绘制,以及野火进展的预测.
- 通过深度学习为推进野火研究提供基础.
主要方法:
- 开发一个覆盖美国野火 (2017-2021) 的多时间遥感数据集.
- 包括表面反射图像和辅助数据 (天气,地形,土地覆盖,燃料).
- 使用多任务深度学习进行像素分类 (检测) 和集成数据建模 (预测).
主要成果:
- 一个71GB的数据集,包含3552张图像和详细的野火生命周期文档.
- 现火 (AF) 和烧伤区域 (BA) 标签的手动质量保证.
- 为三个受支持的野火研究任务建立了基准.
结论:
- 提出的数据集和基准是基于深度学习的野火研究的基础.
- 这种资源有助于在积极的火灾检测,被烧毁区域的绘制和野火预测方面取得进展.
- 通过集成的数据和先进的建模,可以实现加强野火监测和预测能力.
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