A Spatial Visual Quality Evaluation Method for an Urban Commercial Pedestrian Street Based on Streetscape Images—Taking Tianjin Binjiang Road as an Example

Abstract

As core public spaces in cities, urban commercial pedestrian streets are important destinations for local residents and foreign tourists, and confusion regarding the visual space of a commercial pedestrian street sends direct environmental warning signals to pedestrians, affecting their visiting decisions. In this paper, through an investigation consisting of the artificial field simulation of the visual perception of pedestrians, we collect the corresponding street images and extract visual elements using the full convolutional network. Semantic segmentation is performed to obtain the visual parameters of the street. According to the quantitative model, the visual elements are matched with geographic elements, and a geographic information database is established to evaluate the spatial visual quality of commercial pedestrian streets. (1) There is obvious spatial heterogeneity in the spatial visual quality of different streets in commercial pedestrian streets. (2) The building heights, street widths, as well as the street vegetation, facilities, and landscape vignettes are spatial elements that shape the spatial visual quality of commercial pedestrian streets. (3) The main distribution of commercial facilities and the distribution of active businesses have an important impact on the degree of crowd gathering in a street space and the visual spatial quality of a street. This paper provides comparable data collection methods and research methods for the visual spatial quality of commercial pedestrian streets. This paper can also provide valuable data for the design, planning, and sustainable renewal management and regulation of the visual perception of commercial pedestrian streets.