HeatCast：覆盖美国124个城市的街区尺度地表温度预测基准
HeatCast: A Benchmark for Neighborhood-Scale LST Forecasting across 124 U.S. Cities
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中文总结 AI 辅助
HeatCast是覆盖美国124城的街区尺度LST预测基准，含多类特征与评估设置，实验显示Earthformer预测性能优于CNN+LSTM，仅用非LST通道即可达相近效果，相关资源已公开。
中文摘要 AI 辅助
地表温度（LST）是一种广泛使用的卫星衍生城市表面热量测量指标，但目前尚无用于30米尺度LST预测的共享基准。现有研究通常仅覆盖1至3个城市、使用公里级产品，或不公开数据与代码。本文提出HeatCast，这是一个基于Landsat的、针对2013年至2025年6月美国124个城市的月度LST预测基准。HeatCast包含30米分辨率月度图块，涵盖LST、高程、地表反射率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，同时配备固定时间划分、按LCZ分层的指标及参考评估工具。本文评估了CNN+LSTM与Earthformer模型的下月预测性能，其中Earthformer的RMSE达7.74K，而CNN+LSTM为10.42K；仅用8个非LST通道进行预测的RMSE为7.72K，仅用LST历史数据的为8.15K，仅用RGB数据的为8.68K。相关数据、代码与权重以MIT许可公开于指定网址。
英文摘要
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.
发表机构
- University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）
- Taylor Geospatial（泰勒地理空间公司）
机构由 AI 辅助整理，请以论文原文为准。
