Paper Info
Title
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.
Year
DOI
Venue
2019
10.1080/13658816.2019.1643024
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
Keywords
Field
DocType
Street view, urban perception, deep learning, urban planning, human-machine adversarial scoring
Human–machine system,Computer science,Urban planning,Artificial intelligence,Deep learning,Perception,Machine learning,Adversarial system
Journal
Volume
Issue
ISSN
33
12
1365-8816
Citations 
PageRank 
References 
0
0.34
0
Authors
9
Name
Order
Citations
PageRank
Yao Yao1745.97
Zhaotang Liang200.68
Zehao Yuan300.68
Qian Shi48313.37
Yongpan Bie500.34
Jinbao Zhang6313.98
Ruoyu Wang711.43
Jiale Wang800.34
Qingfeng Guan9168.64