The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Streetscore -- Predicting the Perceived Safety of One Million Streetscapes
Open accessAbstract
Social science literature has shown a strong connection between the visual appearance of a city's neighborhoods and the behavior and health of its citizens. Yet, this research is limited by the lack of methods that can be used to quantify the appearance of streetscapes across cities or at high enough spatial resolutions. In this paper, we describe 'Streetscore', a scene understanding algorithm that predicts the perceived safety of a streetscape, using training data from an online survey with contributions from more than 7000 participants. We first study the predictive power of commonly used image features using support vector regression, finding that Geometric Texton and Color Histograms along with GIST are the best performers when it comes to predict the perceived safety of a streetscape. Using Streetscore, we create high resolution maps of perceived safety for 21 cities in the Northeast and Midwest of the United States at a resolution of 200 images/square mile, scoring ~1 million images from Google Streetview. These datasets should be useful for urban planners, economists and social scientists looking to explain the social and economic consequences of urban perception.
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Cited by
Street view imagery in urban analytics and GIS: A review
Biljecki, F., & Ito, K. (2021) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2021.104217 · Cited by 925
Measuring human perceptions of a large-scale urban region using machine learning
Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2018.08.020 · Cited by 810
Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1700035114 · Cited by 518
Deep Learning the City: Quantifying Urban Perception at a Global Scale
Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_12 · Cited by 513
Self-citationYe, Y., Richards, D., Lu, Y., Song, X., Zhuang, Y., Zeng, W., & Zhong, T. (2019) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2018.08.028 · Cited by 366
Metadata
- Authors
- Naik, N., Philipoom, J., Raskar, R., & Hidalgo, C.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 793-799
- OpenAlex
- W1979728958
- Cited by
- 453
- References
- 25
- FWCI
- 45.37
- Type
- conference paper
- Language
- en
- Open access
- green
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- Naik, N., Philipoom, J., Raskar, R., & Hidalgo, C. (2014). Streetscore -- Predicting the Perceived Safety of One Million Streetscapes. 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops. https://doi.org/10.1109/cvprw.2014.121
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