Assessing visual similarity of neighbourhoods with street view images and deep learning techniques

Despite the wide availability of street-view data and advanced computational techniques, the topic of perceived visual similarity in urban design has received little attention. The impact of visual sameness on the loss of urban identity and its effect on individuals' health has been widely deba...

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Bibliographic Details
Published in:Journal of urban design Vol. 30; no. 4; pp. 520 - 531
Main Authors: Verma, Deepank, Mumm, Olaf, Carlow, Vanessa Miriam
Format: Journal Article
Language:English
Published: Routledge 04.07.2025
Subjects:
deep learning
monotony
perception
Similarity
street view imagery
urban design
ISSN:1357-4809, 1469-9664
Online Access:Get full text
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Summary:Despite the wide availability of street-view data and advanced computational techniques, the topic of perceived visual similarity in urban design has received little attention. The impact of visual sameness on the loss of urban identity and its effect on individuals' health has been widely debated. However, empirical evidence to support these arguments has been limited. This study proposes a set of tools to measure similarity in urban neighbourhoods. It utilizes Street view images and DL models such as semantic segmentation and generative inpainting for image enhancement and refinement. It further employs the LPIPS, a DL-based metric that computes image-based perceptual similarity.
ISSN:1357-4809
1469-9664
DOI:10.1080/13574809.2024.2357804