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

Abstract

<p>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.</p>