Use of Tencent Street View Imagery for Visual Perception of Streets
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TLDR
A new type of data for landscape study is suggested, and a technique for automatic information acquisition to determine the visual perception of streets is provided, which can effectively reflect the visual attributes of streets.Abstract:
The visual perception of streets plays an important role in urban planning, and contributes to the quality of residents’ lives. However, evaluation of the visual perception of streetscapes has been restricted by inadequate techniques and the availability of data sources. The emergence of street view services (Google Street View, Tencent Street View, etc.) has provided an enormous number of new images at street level, thus shattering the restrictions imposed by the limited availability of data sources for evaluating streetscapes. This study explored the possibility of analyzing the visual perception of an urban street based on Tencent Street View images, and led to the proposal of four indices for characterizing the visual perception of streets: salient region saturation, visual entropy, a green view index, and a sky-openness index. We selected the Jianye District of Nanjing City, China, as the study area, where Tencent Street View is available. The results of this experiment indicated that the four indices proposed in this work can effectively reflect the visual attributes of streets. Thus, the proposed indices could facilitate the assessment of urban landscapes based on visual perception. In summary, this study suggests a new type of data for landscape study, and provides a technique for automatic information acquisition to determine the visual perception of streets.read more
Citations
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Journal ArticleDOI
Street view imagery in urban analytics and GIS: A review
Filip Biljecki,Koichi Ito +1 more
TL;DR: In this article, a comprehensive systematic review of the state of the art of how street-level imagery is currently used in studies pertaining to the built environment is presented, showing that street view imagery is now clearly an entrenched component of urban analytics and GIScience.
Journal ArticleDOI
A human-machine adversarial scoring framework for urban perception assessment using street-view images
Yao Yao,Yao Yao,Zhaotang Liang,Zehao Yuan,Penghua Liu,Yongpan Bie,Jinbao Zhang,Jinbao Zhang,Ruoyu Wang,Ruoyu Wang,Jiale Wang,Qingfeng Guan +11 more
TL;DR: A human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedback with recommendation scores is described, which allows for the rapid and cost-effective assessment of the local urban perceptions for Chinese cities.
Journal ArticleDOI
How Green Are the Streets Within the Sixth Ring Road of Beijing? An Analysis Based on Tencent Street View Pictures and the Green View Index.
TL;DR: This case study demonstrates that the GVI can effectively represent the quantity of visual Greenery along roads and can be employed to compare street-level visual greenery among different areas or road types and to support urban green space planning and management.
Journal ArticleDOI
Do street-level scene perceptions affect housing prices in Chinese megacities? An analysis using open access datasets and deep learning.
TL;DR: In this paper, a deep learning framework and massive Baidu street view panoramas were employed to visualize and quantify three major scene perception characteristics (greenery, sky and building view indexes, abbreviated GVI, SVI and BVI, respectively) at the street level.
Journal ArticleDOI
Discovering the homogeneous geographic domain of human perceptions from street view images
TL;DR: A novel method for combining human perceptions and the topology of urban roads could identify the homogeneous perception domain, which is valuable for urban structure studies and human perception assessment.
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