Satellite-Based Urban Heat Island Mapping Using Multitemporal Thermal Imagery and AI Models - A Comprehensive Review

Review Article

Abstract

Urban Heat Islands (UHIs) are a significant consequence of rapid urbanization, contributing to environmental and health-related challenges in metropolitan regions. With advances in remote sensing and artificial intelligence (AI), satellite-based thermal imagery has become a vital tool for detecting, monitoring, and analyzing UHIs. This review presents an in-depth synthesis of the methodologies, satellite platforms, thermal indices, AI-based modeling techniques, and current trends used in UHI mapping. It explores the potential of multitemporal thermal datasets, discusses the limitations of traditional methods, and highlights the emerging role of machine learning (ML) and deep learning (DL) models in improving UHI analysis accuracy and resolution. Furthermore, the review examines the integration of AI with big data platforms for large-scale urban monitoring and the importance of high-resolution spatiotemporal datasets for climate-responsive urban planning. The findings underscore the growing importance of data-driven approaches in understanding urban thermal dynamics and offer future directions for real-time UHI assessment, policy development, and sustainable urban design.