首个面向124座美国城市30米地表温度预测的基准数据集
HeatCast: A Benchmark for Neighborhood-Scale LST Forecasting across 124 U.S. Cities
- 构建覆盖124城的高分辨率月度地表温度数据集
- 地球模型在预测中误差低至7.74K，优于传统CNN+LSTM
- 提供完整代码与权重，支持城市热环境研究
地表温度（LST）是广泛使用的卫星反演城市地表热状况指标，但缺乏30米尺度的共享预测基准。以往研究多限于1-3个城市，使用千米级产品，或未公开数据与代码。我们提出HeatCast，基于Landsat的月度LST预测基准，覆盖2013年至2025年6月的124座美国城市。该数据集包含30米分辨率的月度栅格，包含LST、高程、表面反射率RGB、三个光谱指数、宽带反照率、质量掩码及本地气候区（LCZ）标签，并提供固定时间划分、基于LCZ分层的评估指标和参考评测工具。我们评估了CNN+LSTM与Earthformer模型的下月预测性能，Earthformer达到7.74K RMSE，优于CNN+LSTM的10.42K。仅使用8个非LST通道即达7.72K误差，优于仅用历史LST的8.15K和仅用RGB的8.68K。数据、代码与权重已通过MIT协议开源，链接：https://doi.org/10.57967/hf/9889。
原文摘要 · Abstract (English)
Land Surface Temperature (LST) is a widely used satellite-derived measure of urban surface heat, but there is no shared benchmark for forecasting it at 30 m. Prior studies usually cover one to three cities, use kilometer-scale products, or do not release data and code. We introduce HeatCast, a Landsat-based benchmark for monthly LST forecasting across 124 U.S. cities from 2013 through June 2025. HeatCast contains 30 m monthly tiles with LST, elevation, surfacereflectance RGB, three spectral indices, broadband albedo, quality masks, and Local Climate Zone (LCZ) labels, together with a fixed temporal split, LCZ-stratified metrics, and a reference evaluation harness. We evaluate a CNN+LSTM and Earthformer on next-month forecasting, where Earthformer reaches 7.74 K RMSE against 10.42 K for the CNN+LSTM. Forecasting from the eight nonLST channels alone reaches 7.72 K, against 8.15 K from LST history and 8.68 K from RGB. The data, code, and weights are released under MIT at https://doi.org/10.57967/hf/9889.
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