arXiv:2606.11534v1 Announce Type: cross 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 va
The increasing availability of multi-sensor observations and the urgent need to address climate change impacts like urban heat are driving the creation of specialized datasets for AI research.
This dataset provides critical, harmonized data for AI applications in urban climate modeling, enabling more accurate predictions and effective mitigation strategies for urban heat.
Researchers now have a consistent, analysis-ready dataset directly applicable to machine learning, reducing data preparation overhead and accelerating urban heat research.
- · AI/ML researchers in climate science
- · Urban planners and policymakers
- · Smart city technology developers
- · Cities unprepared for urban heat
- · Traditional climate modeling methods
Improved accuracy in predictive models for urban heat islands.
Development of more effective and targeted urban planning policies to mitigate heat.
Enhanced resilience of urban populations to climate change impacts, potentially influencing migration patterns.
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