The DOI matches its Crossref record; 47 of the 47 references it cites were found in a registry or an index.
A human-machine adversarial scoring framework for urban perception assessment using street-view images
Abstract
Though global-coverage urban perception datasets have been recently created using machine learning, their efficacy in accurately assessing local urban perceptions for other countries and regions remains a problem. Here we describe a human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedback with recommendation scores, which allows for the rapid and cost-effective assessment of the local urban perceptions for Chinese cities. Using the state-of-the-art Fully Convolutional Network (FCN) and Random Forest (RF) algorithms, the proposed method provides perception estimations with errors less than 10%. The driving factor analysis from both the visual and urban functional aspects demonstrated its feasibility in facilitating local urban perception derivations. With high-throughput and high-accuracy scorings, the proposed human-machine adversarial framework offers an affordable and rapid solution for urban planners and researchers to conduct local urban perception assessments.
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Cited by
Son, T. H., Weedon, Z., Yigitcanlar, T., Sanchez, T., Corchado, J. M., & Mehmood, R. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2023.104562 · Cited by 396
A review of urban physical environment sensing using street view imagery in public health studies
Kang, Y., Zhang, F., Gao, S., Lin, H., & Liu, Y. (2020) · Annals of GIS · doi:10.1080/19475683.2020.1791954 · Cited by 292
Metadata
- Authors
- Yao, Y., Liang, Z., Yuan, Z., Liu, P., Bie, Y., Zhang, J., Wang, R., Wang, J., & Guan, Q.
- Venue
- International Journal of Geographical Information Science
- Publisher
- Informa UK Limited
- Published
- 2019-07-19
- Volume
- 33
- Issue
- 12
- Pages
- 2363-2384
- ISSN
- 0269-3798 (ISSN-L)
- 1365-8816
- 1362-3087
- OpenAlex
- W2962764844
- Cited by
- 425
- References
- 47
- FWCI
- 16.92
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yao, Y., Liang, Z., Yuan, Z., Liu, P., Bie, Y., Zhang, J., Wang, R., Wang, J., & Guan, Q. (2019). A human-machine adversarial scoring framework for urban perception assessment using street-view images. International Journal of Geographical Information Science. https://doi.org/10.1080/13658816.2019.1643024
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