Harnessing geographic information system and street view imagery for thermal gradient distribution auditing

Abstract

Assessing and managing the thermal environment within urban streetscapes is of paramount importance for the health, livability, and ecological sustainability of metropolitan regions. However, due to a scarcity of high-precision historical street thermal environment data for prediction and modeling, existing urban thermal environment classification assessment studies suffer from low resolution (> 30 m) or limited research scope (e.g., community-level), resulting in less accurate and comprehensive insights. This study introduces an innovative framework for constructing large-scale urban street-level thermal gradients using classified samples derived from the spatial structural features of street points. The core of this framework lies in the k-means unsupervised classification algorithm. This approach integrates detailed local geographic information system (GIS) data with street view features, calculated through semantic segmentation of Google Street-View-Panorama using the DeepLabV3 model. This allows for the categorization of a vast array of high-precision street points based on spatial structural similarity, a key factor influencing the similarity of street thermal environment features. By selecting appropriate samples for on-site thermal environment measurements within each category and subsequently extrapolating this knowledge to the thermal environment classification of each category, this framework facilitates the rapid creation of high-precision street-level thermal gradient models across extensive urban areas.