Mapping sense of place as a measurable urban identity: Using street view images and machine learning to identify building façade materials

Abstract

Sense of place, as an intangible perception, is widely recognized as an urban identity and of great value in both cross-cultural studies and contemporary urbanism. Building façade material can effectively capture sense of place due to its combination of physical and social attributes. Nevertheless, there are no widely implementable and high-resolution approaches to identify façade materials on a large scale. As a response, this study proposes a method using street view images (SVIs) and a set of deep Convolutional Neural Networks (CNNs) to identify building façade materials. Specifically, a large cross-cultural training set was built to promote generalizability. Buildings within SVIs were divided into high-resolution rectangular images and classified using a well-trained Residual Network-50 (ResNet-50) model. Sense of place and its spatial patterns were then depicted by measuring façade material and analytical indicators including diversity and continuity. Eight cities worldwide with distinctive urban identities were examined. The findings revealed that compared to Asian cities, New York City, Chicago, and London are similar, while Paris and Tokyo are more distinctive. While challenges persist in comprehensively measuring the sense of place, the analysis of façade materials offers an insightful indicator that can assist in enhancing urban identity for contemporary urbanism. This study not only promotes the fine development of urban science through the empowerment of intelligent algorithms but also introduces a new perspective on exploring unmeasurable qualities based on the objective physical environment.