构建48城2022-2023年城市热力数据立方体，助力机器学习研究
Urban Heat MiniCubes: An AI-Ready dataset for urban heat research
- 整合多源遥感数据，统一网格对齐，减少预处理负担
- 覆盖48城，提供高时空分辨率双模态数据（地表反射率与红外温度）
- 公开可复用，适配城市热环境建模、气候风险评估等场景
城市热岛效应受不透水面和复杂建筑环境加剧，但街级热变异难以量化，因多传感器观测常缺乏一致、分析就绪的数据。本文发布「Urban Heat MiniCubes」——一个面向机器学习的城市热研究公共数据集，支持FAIR原则。该数据集提供西半球48个城市的90×90公里格网化数据立方体，时间跨度为2022–2023年，所有变量已重投影并空间对齐，降低预处理成本（如重投影、重采样和时空对齐）。包含两类互补模态：(i) 高空间分辨率、低频率的Landsat 8/9（如地表反射率）与Sentinel-1（如合成孔径雷达后向散射）；(ii) 高时间频率、粗分辨率的GOES-R（如长波红外亮温）与微波地表温度产品。本文记录变量与元数据，通过变量间分析与基于自编码器的重建误差总结（按像素类别如水体、云等划分），评估数据质量。同时讨论潜在应用与局限性。
原文摘要 · Abstract (English)
Urban heat is amplified by impermeable surfaces and heterogeneous built environments, yet street-level variability remains difficult to quantify because multi-sensor observations are rarely available in consistent, analysis-ready form at the necessary spatiotemporal scales. We present "Urban Heat MiniCubes," a publicly available, FAIR-oriented dataset designed for machine learning applications in urban heat research. The dataset provides harmonized 90 x 90 km gridded data cubes for 48 cities in the Western Hemisphere spanning 2022-2023, with variables reprojected and collocated to a common grid to reduce preprocessing (e.g., reprojection, resampling, and spatiotemporal alignment). Urban Heat MiniCubes includes two complementary modalities: (i) higher-spatial-resolution, lower-frequency observations from Landsat 8/9 (e.g., surface reflectances) and Sentinel-1 (e.g., synthetic aperture radar backscatter), and (ii) higher-temporal-frequency, coarser observations from GOES-R (e.g., longwave infrared brightness temperatures) and a microwave land surface temperature product. We document variables and metadata and provide technical assessment using inter-variable analyses and autoencoder-based reconstruction-error summaries across pixel classes (e.g., water and cloud). Potential use cases and limitations are also discussed.
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