A human-machine adversarial scoring framework for urban perception assessment using street-view images
By Yao Yao, Zhaotang Liang, Zehao Yuan, Penghua Liu, Yongpan Bie, Jinbao Zhang, Ruoyu Wang, Jiale Wang, Qingfeng Guan. International Journal of Geographical Information Science, 2019
384 citations recorded by the source corpus.
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.
Subfields: Atmospheric Science, Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/19a2435a002787c7b20b8e5f20371e72fb8e7b9e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- Analyzing the correlation between visual space and residents' psychology in Wuhan, China using street-view images and deep-learning technique (2021, positive): “To train the Random Forest model, we use a human-machine adversarial method to ensure accuracy [22], as shown in Fig.”
- Street view imagery in urban analytics and GIS: A review (2021, positive): “…Ma, Li, & Rao, 2020; Ye, Zeng, Shen, Zhang, & Lu, 2019), and researchers have been using SVI to measure urban perceptual attributes such as safety and wealth, vibrancy, comfort, and attitude towards greenery (Min, Mei, Liu, Wang, & Jiang, 2020; Yao et al., 2019; Wang et al., 2019; Fu & Song, 2020).”