PyroStack：美国野火的多波段时空亚日数据集
PyroStack: A Multi-Band Spatio-Temporal Sub-Daily Dataset for Wildfires in the United States
- University of California, Irvine（加州大学尔湾分校）
- Columbia University（哥伦比亚大学）
- Spatial Informatics Group（空间信息学集团）
- CloudFire Inc.（CloudFire公司）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
PyroStack数据集整合多源遥感与环境数据，覆盖美国6994起野火，提供多分辨率时空信息，支持物理与机器学习模型的开发与评估，以提升野火蔓延预测能力。
AI中文摘要：
野火对生态系统、空气质量和人类系统构成的危害日益加剧，这催生了对支持系统开发和评估跨多样化景观火灾蔓延预测模型的数据集的迫切需求。有效的预测需要在适合物理模拟和数据驱动方法的空间和时间分辨率上整合气象条件、燃料、植被和地形等因素。然而，现有数据集往往缺乏捕捉这些相互作用控制因素所需的分辨率和覆盖范围。PyroStack数据集通过提供美国本土及阿拉斯加地区统一的、基于事件的野火与环境数据集合，填补了这一空白。该数据集将卫星观测的火灾数据与大气再分析、植被、燃料特征和地形信息整合到一个统一框架中，涵盖了2012年至2024年间发生的6994起野火，涉及广泛的生态系统和气候条件。PyroStack提供从30米到9公里的空间分辨率以及小时级的时间分辨率，并以12小时间隔提供火灾蔓延数据，以支持模型初始化和评估。通过将广泛的空间覆盖与精细的时空细节相结合，该数据集能够系统分析野火动态，并支持基于物理的模型和机器学习方法，为基准测试和改进火灾蔓延模型奠定了基础，未来的扩展旨在纳入更多区域和火灾扑救数据流，以进一步推进野火预测。
英文摘要：
Wildfires are an increasing hazard to ecosystems, air quality, and human systems, creating a growing need for datasets that support systematic development and evaluation of models for predicting fire spread across diverse landscapes. Effective prediction requires integrating meteorological conditions, fuels, vegetation, and topography at spatial and temporal resolutions suitable for both physical simulation and data-driven approaches. However, existing datasets often lack the resolution and coverage needed to capture these interacting controls. The PyroStack dataset addresses this gap by providing a harmonized, event-based collection of wildfire and environmental data across the contiguous United States and Alaska. It integrates satellite-derived fire observations with atmospheric reanalysis, vegetation, fuel characteristics, and topographic information into a unified framework spanning 6994 wildfires that occurred between 2012 and 2024 across a wide range of ecosystems and climate conditions. PyroStack offers spatial resolutions ranging from 30 m to 9 km and hourly temporal resolution, along with fire progression data at 12-hour intervals to support model initialization and evaluation. By combining broad spatial coverage with fine spatial and temporal detail, the dataset enables systematic analysis of wildfire dynamics and supports both physics-based and machine learning approaches, providing a foundation for benchmarking and improving fire spread models, with future extensions aimed at incorporating additional regions and fire suppression data streams to further advance wildfire prediction.
