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ISPRS Int. J. Geo-Inf. 2017, 6(9), 265; doi:10.3390/ijgi6090265

Use of Tencent Street View Imagery for Visual Perception of Streets

1,2,3,4
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1,2,4,* , 1,4
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1,4
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1,4
and
1,4,*
1
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210093, China
2
Collaborative Innovation Center for the South Sea Studies, Nanjing University, Nanjing 210093, China
3
Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing University, Nanjing 210093, China
4
Department of Geographic Information Science, Nanjing University, Nanjing 210093, China
*
Authors to whom correspondence should be addressed.
Received: 18 July 2017 / Revised: 11 August 2017 / Accepted: 21 August 2017 / Published: 25 August 2017
View Full-Text   |   Download PDF [9623 KB, uploaded 25 August 2017]   |  

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. View Full-Text
Keywords: streetscapes; visual perception; Tencent Street View; street level streetscapes; visual perception; Tencent Street View; street level
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Cheng, L.; Chu, S.; Zong, W.; Li, S.; Wu, J.; Li, M. Use of Tencent Street View Imagery for Visual Perception of Streets. ISPRS Int. J. Geo-Inf. 2017, 6, 265.

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