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The Urban Heat Island (UHI) effect leads to increased energy consumption, and a decline in urban residents' quality of life. Therefore, quantitatively analyzing this phenomenon and developing mitigation strategies is of critical importance. This study applies a Geo-Explainable Artificial Intelligence (GeoXAI) approach to quantify the influence of urban spatial configurations and land use characteristics on Land Surface Temperature (LST). LST was derived from Landsat 8 satellite imagery. Independent variables included vegetation indices such as the Normalized Difference Built-up Index (NDBI), and Green Normalized Difference Vegetation Index (GNDVI), as well as digital elevation models (DEM) and land cover data. Four tree-based machine learning models were compared. Among them, XGBoost demonstrated the highest prediction accuracy with an R² value of 0.9885 at the 150 m buffer distance. Additionally, the application of Shapley Additive Explanations (SHAP) identified NDBI, GNDVI, elevation (DEM), and roads as the most influential variables on LST. Furthermore, a scenario simulating the underground conversion of major arterial roads in Seoul and the restoration of the surface into urban parks revealed an LST reduction effect of approximately 0.45–1 °C, depending on vegetation density. These findings underscore the importance of green space restoration in mitigating the UHI effect.
Cho et al. (Mon,) studied this question.