Use of Tencent Street View Imagery for Visual Perception of Streets

TLDR

The visual perception of streets is crucial for urban planning and residents’ quality of life, yet evaluation has been constrained by inadequate techniques and limited data; the advent of street‑view services has provided abundant street‑level images that overcome these restrictions. The study aimed to analyze urban street visual perception using Tencent Street View images and proposed four indices—salient region saturation, visual entropy, green view, and sky‑openness—to characterize it. The authors applied Tencent Street View images from Jianye District, Nanjing, to compute the four proposed indices. The experiment showed that the four indices effectively reflect street visual attributes, enabling assessment of urban landscapes based on visual perception and demonstrating the usefulness of Tencent Street View data for automatic visual perception analysis.

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.