Detection and Characterization of Urban Heat Islands with Machine Learning

Muaaz Bhamjee, Hiyam Debary, Zaheed Gaffoor, Tamara Govindasamy, Craig Mahlasi, Mustansar Fiaz, Etienne E. Vos, Levente J. Klein, Sibusisiwe Makhanya, Campbell D. Watson, Julian Kuehnert

Abstract: 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.