International Journal of Science, Engineering and TechnologyAuthors: Krishna Agarwal, Aman Shaw, Shwet Kashyap
Abstract: Urban Heat Island (UHI) is an established environmental phenomenon where urban environments experience substantially higher temperatures than their surrounding rural environments, which is directly attributed to the high density of infrastructure and minimal vegetation, along with anthropogenic heat emissions. Accurate identification of UHI regions is crucial for effective urban planning. Conventional UHI identification methods based on threshold value analysis are ineffective and do not account for spatial continuity. This study presented an effective and efficient framework that combined satellite data with physical models and deep learning to identify UHI regions. Landsat-8 thermal and optical data were employed to derive Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) values. A rule-based approach was initially employed to generate weak UHI labels, which were then refined using a U-Net convolutional neural network (CNN). Experimental results across five diverse global cities demonstrate that the proposed method is highly effective for UHI identification. By leveraging physical regularization, it resolves the noise overfitting of traditional neural baselines—outperforming them in classification calibration (ROC-AUC and AUPRC) and cross-city generalizability—while entirely eliminating the need for manual pixel-level annotation.
DOI: http://doi.org/10.5281/zenodo.21701657
