ArticleProceedings of the National Academy of Sciences of the United States of America2023
Urban visual intelligence: Uncovering hidden city profiles with street view images.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Measuring Street Built Environments for Children's Use: A Systematic Review of Measurement Tools.Journal of urban health : bulletin of the New York Academy of Medicine · 2025Pooled it
- Reassessing the Heterogeneous Effects of Greenspace on Air Pollution Conditioned on Seasons, Vegetation Types, and Street Structures.Environmental science & technology · 2026Article
- Assessing greenspace and cardiovascular disease risk through deep learning analysis of street-view imagery in the US-based nationwide Nurses' Health Study.Environmental epidemiology (Philadelphia, Pa.) · 2026Article
- Community-level education percentile rank estimation in China using multi-source big data and machine learning.Scientific data · 2026Article
- Whose city is it? Mapping perceived urban livability with citizen-guided AI.npj urban sustainability · 2026Article
- Deep learning assessment of street spatial quality in old residential communities of Wuchang, Wuhan, China.Scientific reports · 2025Article
- Declining subjective well-being disparities concurrent with urbanization in China.National science review · 2025Article
- The First Seasonal Green View Index Mapping Dataset across Chinese cities powered by deep learning.Scientific data · 2025Article
- Urban fabric decoded: High-precision building material identification via deep learning and remote sensing.Environmental science and ecotechnology · 2025Article
- Reducing the social inequity of neighborhood visual environment in Los Angeles through computer vision and multi-model machine learning.Sustainable cities and society · 2025Article
- Urban walkability through different lenses: A comparative study of GPT-4o and human perceptions.PloS one · 2025Article
- Artificial intelligence for geoscience: Progress, challenges, and perspectives.Innovation (Cambridge (Mass.)) · 2024Review
- Article
- Putting ChatGPT vision (GPT-4V) to the test: risk perception in traffic images.Royal Society open science · 2024Article
- An annotated street view image dataset for automated road damage detection.Scientific data · 2024Article
- Revisiting the impact of public spaces on the mental health of rural migrants in Wuhan: an integrated multi-source data analysis.International journal of health geographics · 2024Article
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4 authors.
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Abstract
A longstanding line of research in urban studies explores how cities can be understood through their appearance. However, what remains unclear is to what extent urban dwellers' everyday life can be explained by the visual clues of the urban environment. In this paper, we address this question by applying a computer vision model to 27 million street view images across 80 counties in the United States. Then, we use the spatial distribution of notable urban features identified through the street view images, such as street furniture, sidewalks, building façades, and vegetation, to predict the socioeconomic profiles of their immediate neighborhood. Our results show that these urban features alone can account for up to 83% of the variance in people's travel behavior, 62% in poverty status, 64% in crime, and 68% in health behaviors. The results outperform models based on points of interest (POI), population, and other demographic data alone. Moreover, incorporating urban features captured from street view images can improve the explanatory power of these other methods by 5% to 25%. We propose "urban visual intelligence" as a process to uncover hidden city profiles, infer, and synthesize urban information with computer vision and street view images. This study serves as a foundation for future urban research interested in this process and understanding the role of visual aspects of the city.
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