A human-machine adversarial scoring framework for urban perception assessment using street-view images. Issue 12 (2nd December 2019)

• Record Type: Journal Article
• Authors: Yao, Yao; Liang, Zhaotang; Yuan, Zehao; Liu, Penghua; Bie, Yongpan; Zhang, Jinbao; Wang, Ruoyu; Wang, Jiale; Guan, Qingfeng
• Is Part Of: International journal of geographical information science. Volume 33:Issue 12(2019)
• Page Start: 2363
• Page End: 2384
• Publication Date: 2019-12-02
• Subjects: Street view -- urban perception -- deep learning -- urban planning -- human-machine adversarial scoring; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1643024
• Languages: English
• ISSNs: 1365-8816

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.