Visualising the Heat: A Street-Level Approach to Urban Temperature Prediction

Urban heat islands (UHIs) pose challenges to urban sustainability and citizen well-being. This study explores the interaction between Land Surface Tempera-ture (LST) and the Green View Index (GVI) in Milan. Using advanced tech-niques such as image segmentation and machine learning, this analysis scrutinises the nuanced relationship between urban temperatures and visual elements ob-served across the four cardinal directions. The processing of street-level images through image segmentation not only reveals substantial correlations, but also discerns varied effects based on image heading. Using spatial regression, the re-sults reveal the substantial impact of different image features, highlighting the predominant influence of greenery on temperatures. Adopting Gradient Boosting for LST prediction, this approach highlights areas where temperature underesti-mation may occur. This method highlights potential biases in citizens' heat estima-tion of heat islands who evaluate surrounding conditions just by observing the environment, underscoring the critical need for real-time georeferenced infor-mation to fortify public awareness and mitigate risks associated with urban heat islands