The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index.
Deep Learning the City: Quantifying Urban Perception at a Global Scale
Abstract
Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city’s physical appearance and the behavior and health of its residents. Yet, the throughput of current methods is too limited to quantify the perception of cities across the world. To tackle this challenge, we introduce a new crowdsourced dataset containing 110,988 images from 56 cities, and 1,170,000 pairwise comparisons provided by 81,630 online volunteers along six perceptual attributes: safe, lively, boring, wealthy, depressing, and beautiful. Using this data, we train a Siamese-like convolutional neural architecture, which learns from a joint classification and ranking loss, to predict human judgments of pairwise image comparisons. Our results show that crowdsourcing combined with neural networks can produce urban perception data at the global scale.
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Cites
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A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
Capponi, A., Fiandrino, C., Kantarci, B., Foschini, L., Kliazovich, D., & Bouvry, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2914030 · Cited by 594
Li, W., & Hsu, C.-Y. (2022) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi11070385 · Cited by 147
Verma, D. (2025) · Journal of Urban Design · doi:10.1080/13574809.2025.2514574 · Cited by 9
Metadata
- Authors
- Dubey, A., Naik, N., Parikh, D., Raskar, R., & Hidalgo, C. A.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 196-212
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2478090196
- Cited by
- 513
- References
- 58
- FWCI
- 48.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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