Integrating planar and vertical environmental features for modelling land surface temperature based on street view images and land cover data

Abstract

With ongoing urbanization, high temperatures occur more frequently in urban environments. The urban heat island (UHI) effect has serious impacts on people's living environment and health, and has become an important concern for climate changes. Existing studies on environmental effects on UHI mainly focus on aspects related to urban horizontal expansion, but investigation into the effects of vertical urban growth (e.g., tall buildings) is limited. This study proposes a comprehensive set of features to characterize the planar and vertical aspects of urban environments by using panoramic Street View images (SVIs) and land cover data, and employs multiple linear regression and machine learning models to model land surface temperature (LST). Furthermore, an explainable AI method is applied to quantify the warming/cooling effects of each individual feature on LST.The results show that the proposed vertical/planar features contribute to the estimation of LST (R2 = 0.693). Adding the vertical features (extracted from SVIs) can improve LST prediction by 9.2% compared to using only planar features. Of all the features, built-up features contribute the most to LST variation, and large trees show strong cooling effects. These results serve as a basis for sustainable urban planning and help mitigate the UHI effect in cities.