October 10, 2025ISPRS International Journal of Geo-InformationOpen Access

Spatiotemporal Analysis of Urban Perception Using Multi-Year Street View Images and Deep Learning

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Authors

WZWen ZhongChongqing University LWLei Wang XHXin Han

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Implication

This study uncovers temporal and spatial changes in urban perception through deep learning analysis of street view images in Shanghai, suggesting implications for urban planning.

Key Points

Average perception scores in Shanghai increased by 4.85% from 2013 to 2019, indicating positive changes over time.
For every 1.5 km increase in distance from the city center, perception scores rose by an average of 0.0241, highlighting spatial variation.
A robust analytical framework using machine learning techniques was developed to effectively assess urban perception changes.
The findings emphasize the importance of natural features in enhancing urban perceptions, informing better urban design practices.

Cite This Study

Zhong et al. (2025) studied this question.

synapsesocial.com/papers/68e861857ef2f04ca37e3bcd https://doi.org/10.3390/ijgi14100390

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Implication

This study uncovers temporal and spatial changes in urban perception through deep learning analysis of street view images in Shanghai, suggesting implications for urban planning.

Spatiotemporal Analysis of Urban Perception Using Multi-Year Street View Images and Deep Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

Spatiotemporal Analysis of Urban Perception Using Multi-Year Street View Images and Deep Learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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