Spatially Explicit Mapping and Assessment of Urban Heat Islands Using AI

Abstract

The Urban Heat Island effect is a threat to urban livability. This phenomenon is most noticeable in dense urban areas, where impervious surfaces absorb and store solar energy as heat, while the cooling benefits of vegetation—shade and evapotranspiration—are diminished. In this study, we integrate satellite-derived datasets with the InVEST Urban Cooling model to quantify heat mitigation potential across the Washington D.C. area. The model computes a Heat Mitigation Index (HMI) by combining land use maps with biophysical drivers: shade, evapotranspiration, albedo, and proximity to “cooling islands.” Generating the high-resolution inputs required (land cover, land surface temperature, albedo, canopy cover) has traditionally been time and labor intensive. Leveraging cloud computing and AI-powered machine learning algorithms, this study develops a model to generate the land use map using Sentinel satellite data. Time-series composites of daytime surface temperature and albedo were computed using the Google Earth Engine cloud computing platform. Results show the average Heat Mitigation Index across the region is 0.30, with tree-covered area achieving values up to 0.54, indicating stronger cooling potential. Simulations reveal that without the influence of vegetation and bodies of water, average urban temperatures rise to 39.17°C, with high-density zones reaching 40.64°C. This approach demonstrates the power of AI-driven analytics to deliver a scalable, data-informed framework that is adaptable to all areas. It enables efficient scenario analysis, supports climate-resilient urban planning, and informs the design of targeted interventions (e.g. green corridors) by identifying priority areas for urban heat risk mitigation and adaptation.