Geospatial urban heat mapping with interpretable machine learning and deep learning: a case study in Hue City, Vietnam

The established data-driven approach, coupled with SHAP, provides a valuable tool for understanding the spatial distribution of LST as well as mapping hot spots that experienced the highest level of urban heat stress, and supports the analysis of mitigation measures for regulating temperature and reducing the impacts of the urban heat island effect.