HeatCast: A Benchmark for Neighborhood-Scale LST Forecasting across 124 U.S. Cities

2026-08-07

AI Summary

This study addresses the lack of a unified, high-resolution, and open-access benchmark for urban land surface temperature (LST) prediction. To this end, it establishes the first Landsat-based 30-meter monthly LST forecasting benchmark covering 124 U.S. cities from 2013 to 2025, integrating multi-channel remote sensing data—including surface reflectance, spectral indices, albedo, and Local Climate Zone (LCZ) labels—and introducing a fixed temporal split with LCZ-stratified evaluation. Experiments using CNN+LSTM and Earthformer models demonstrate that Earthformer achieves an RMSE of 7.74 K for next-month LST prediction; notably, using only non-LST input channels yields an RMSE of 7.72 K, substantially outperforming approaches relying solely on historical LST or RGB data, thereby confirming the efficacy of multi-source non-LST information for accurate LST forecasting.

Abstract

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.

Problem

• Land Surface Temperature
• urban heat
• forecasting benchmark
• neighborhood-scale
• satellite data

Innovation

• LST forecasting
• neighborhood-scale benchmark
• multi-source remote sensing
• Earthformer
• Local Climate Zones