A Multidimensional Framework for Diagnosing Streetscape Perception in Historic-District Renewal Using Street-View Imagery and Deep Learning

Abstract

The renewal of historic districts needs to address human-centered issues, such as cultural expression, spatial experience, and visual comfort, at the street scale. However, existing assessment methods predominantly rely on field surveys, expert judgment, or individual visual indicators, making it difficult to produce reproducible and spatially explicit diagnostic evidence across extensive street networks. This study proposes a multidimensional streetscape perception diagnostic framework that evaluates three dimensions: cultural character recognition (CCR), spatial order (SO), and visual comfort (VC). Taking the Pengcheng Qili historic district in Xuzhou, China, as a case study, the framework integrates street-view imagery, subjective pairwise comparisons, the Bradley–Terry model, and ResNet50-based deep learning prediction. Based on 2243 sampling points and 8693 street-view images, three perception–prediction models were developed and evaluated using five-fold stratified cross-validation, followed by independent external validation using additional historic-district images. The outputs were subsequently mapped onto the street network and examined using spatial statistical analysis and multidimensional profile classification. The results show that the three perception dimensions exhibit distinct spatial patterns and significant spatial clustering. CCR forms localized clusters around historical nodes and heritage-rich areas, whereas SO and VC show clearer corridor-like and network-like patterns. The proposed framework organizes streetscape perception predictions into interpretable multidimensional spatial profiles, thereby providing spatial evidence for conservation-oriented renewal and fine-scale governance of historic districts.