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A human-machine adversarial scoring framework for urban perception assessment using street-view images

Authors:	Yao Yao Zhaotang Liang Zehao Yuan Penghua Liu Yongpan Bie Jinbao Zhang

Institution:	1. School of Geography and Information Engineering, China University of Geosciences, Wuhan, Hubei, China;2. Alibaba Group, Hangzhou, Zhejiang, Chinahttps://orcid.org/0000-0002-2830-0377;3. Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Konghttps://orcid.org/0000-0001-9261-5261;4. School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong, Chinahttps://orcid.org/0000-0002-8574-891X;5. School of Geography and Planning, Sun Yat-sen University, Guangzhou, Guangdong, China;6. Tencent Technology Inc., Shenzhen, Guangdong, Chinahttps://orcid.org/0000-0001-8510-149X

Abstract:	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.

Keywords:	Street view urban perception deep learning urban planning human-machine adversarial scoring

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