Assessing the effect of street-level urban morphology on Land Surface Temperature

USING STREET-VIEW IMAGES TO MAP URBAN MORPHOLOGY TO INVESTIGATE THE EFFECTS ON URBAN TEMPERATURES IN AMSTERDAM

According to the latest IPCC reports, heat waves will increase in both intensity and frequency. Due to the physical nature of cities, they are particularly vulnerable to this increase in heat. Urban heat island (UHI) effect analysis often relies on satellite imagery, which gives a planar representation of often three-dimensional features. In this study, we propose to integrate street view data to create a large-scale approximation of local street-level micro-climates in urban environments, using Amsterdam as a case study. We present a method that incorporates street view images with a semantic segmentation model to capture finer urban elements from the panoramic images, such as sky, buildings, trees, and pervious and impervious surfaces. Furthermore, our approach also involves the calculation of view factors derived from panoramic street view images, employing a hemispherical azimuthal projection technique to accurately capture the 3D element of the urban environment. This allows us to assess the impact of various environmental features on LST, considering elements such as tree view factor (TVF), sky view factor (SVF), and building view factor (BVF). We then use the extracted features to model the relationship between these features and LST using machine learning algorithms such as Support Vector Regression, Gradient Boosting Tree, and a Random Forest. The Random Forest model turned out to be the best-performing model. To address research questions, these features are analyzed for their correlation with Land Surface Temperature (LST). The study reveals strong correlations between LST and buildings/trees, while the sky correlation is surprisingly weak. The SVF-LST relationship is intricate, with larger SVF leading to increased heat absorption yet potentially diminishing the UHI effect through enhanced airflow. Application of results in the Amsterdam context demonstrates practical insights for climate adaptation. Areas with low tree or pervious surface coverage exhibit higher LST values, emphasizing the importance of urban greening for heat mitigation. Additionally, the study uncovers unexpected hotspots, challenging existing climate strategies. However, limitations exist, such as LST not directly equating to thermal comfort, potential exaggeration of cooling effects, and the focus on summertime daytime temperatures.