Local Climate Zones and Land Surface Temperature Pairs Dataset of Five Southeast Asian Cities for UHI Modelling

Abstract Urban heat islands (UHIs) pose significant environmental challenges in rapidly urbanising regions, highlighting the need for high-quality spatial datasets to better characterise urban thermal patterns. The Local Climate Zones (LCZ) approach provides a standardised land-use framework for UHI research and is known to strongly correlate with Land Surface Temperature (LST). In this study, 27 LCZ maps for five Southeast Asian cities were generated using cloud-free Landsat 8/9 scenes. Corresponding LST maps were retrieved to construct a paired dataset. Spearman’s rank correlation analysis confirmed a similar relationship between LCZ classes and LST existed in all pairs, shown by Spearman’s r ranging from -0.675 to -0.874, indicating a strong negative direction. When evaluated using the same testing polygons, our LCZ maps consistently outperformed the existing global LCZ dataset. A fully convolutional network was trained using the dataset to perform pixel-wise LST regression using LCZ maps, achieving an RMSE of 1.371℃ and an R 2 of 0.891 under random-split setting and RMSE values ranging from 1.212 °C to 1.623 °C across individual city validations. The results highlight the substantial predictive performance of LCZ information for spatial LST estimation and demonstrate the suitability of the dataset for UHI machine-learning applications. The dataset is publicly available at https://doi.org/10.5281/zenodo.18223368 . Graphical Abstract Based on the graphical abstract snapshot, this study utilises Landsat 8 and 9 to develop a paired Local Climate Zones (LCZ) and Land Surface Temperature (LST) for five major Southeast Asian Cities. For each city, all Landsat 8/9 scenes from 2013 to 2025 were retrieved and clipped to the city extent and checked for cloud cover, if the cloud cover is less than 10%, the scene is selected for LCZ mapping through a Convolutional Neural Network (CNN) using our carefully-selected LCZ polygons, if the validation overall accuracy of the mapped LCZ scene is greater than or equal to 70%, the corresponding LST scene is calculated and clipped to form an LCZ and LST pair. Spearman’s rank correlation analysis resulted in an average Spearman’s rank of -0.783 across all generated pairs. The formed SEA-LCZ-LST dataset was tested for pixel-wise LST regression from LCZ using a Fully Convolutional Network (FCN) and achieved a RMSE of 1.371℃ and R2 of 0.891 under random-split setting. These findings demonstrate the dataset’s suitability for machine-learning-based UHI applications in the Southeast Asian region.