Detection and Characterization of Urban Heat Islands with Machine Learning

Assessing and understanding the urban scale impacts of extreme climate events is a global necessity. Risks associated with heat, where intra-urban dynamics and rural/urban boundary conditions greatly impact its distribution, are of particular interest as the evolution of climate change and ur-banization persists. Characterizing Urban Heat Island (UHI) effects is dependent on the availability of high-resolution near-surface air temperature maps and a description of the Local Climate Zones (LCZs). This study assesses the applicability of state-of-the-art (SOTA) Artificial Intelligence (AI) techniques for UHI detection and characterization. A Geospatial Foundation Model (GFM) is fine-tuned to predict 2 m air temperature at a 1 km resolution for the urban areas of Johannesburg, South Africa, with mean absolute error measures less than 1.5 °C. UHI characterization is further enabled through a Fully Connected Network (FCN) model for LCZs classification for the same region of interest.