Artificial Intelligence-Based Modeling of Urban Heat Islands Using Remote Sensing and Geospatial Data for Sustainable Urban Planning

Urban Heat Islands (UHIs) have become one of the most critical environmental consequences of rapid urbanization, significantly affecting human health, energy consumption, ecosystem stability, and urban sustainability. Increasing concentrations of impervious surfaces, dense building configurations, anthropogenic heat emissions, and declining vegetation cover intensify land surface temperatures in metropolitan regions, making cities more vulnerable to climate change and extreme heat events. Conventional approaches to UHI assessment primarily rely on field observations and statistical analyses, which often lack sufficient spatial coverage, temporal continuity, and predictive capability. Recent advances in Artificial Intelligence (AI), remote sensing, Geographic Information Systems (GIS), and geospatial analytics have fundamentally transformed urban climate research by enabling continuous monitoring, high-resolution spatial modeling, automated feature extraction, and predictive simulation of urban thermal environments. Machine learning and deep learning algorithms integrate satellite imagery, meteorological observations, land-use information, digital elevation models, and socioeconomic datasets to improve understanding of urban thermal dynamics and support evidence-based planning decisions. Meanwhile, GIS platforms provide powerful spatial analysis capabilities, whereas remote sensing technologies enable repeated observations of land surface temperature across diverse temporal and spatial scales. This review comprehensively synthesizes recent scientific developments concerning AI-based Urban Heat Island modeling using remote sensing and geospatial data. It examines theoretical foundations, remote sensing datasets, machine learning algorithms, GIS integration, practical planning applications, climate adaptation strategies, implementation challenges, and future research directions. The findings indicate that integrated AI-geospatial frameworks substantially improve prediction accuracy, support climate-resilient urban planning, enhance environmental monitoring, and facilitate sustainable city development. Nevertheless, challenges associated with data quality, model transferability, computational complexity, interoperability, and ethical governance continue to require further investigation.

کلیدواژه ها:

Artificial Intelligence ، Urban Heat Island ، Remote Sensing ، Geographic Information Systems.