Urban Heat MiniCubes: An AI-Ready dataset for urban heat research

• URL: http://arxiv.org/abs/2606.11534v1
• Date: Wed, 10 Jun 2026 00:34:20 GMT
• Title: Urban Heat MiniCubes: An AI-Ready dataset for urban heat research
• Abstract summary: "Cube Heat Mini-Cubes" is a publicly available dataset for machine learning applications in urban heat research.<n>The dataset provides 90 x 90 km gridded data cubes for 48 cities in the Western Hemisphere spanning 2022-2023.
• Score: 35.306432670332065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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