Street space visual quality evaluation method of tourism sites based on street view images

Street space is the main space that affects tourists' experience of tourism sites. The visual quality of street space is crucial to the development of tourism. However, the evaluation method of visual quality needs further exploration. This paper selected Gulangyu, the famous tourism site in Xiamen, as a study case. First, we established a quantitative model of visual quality combining the existing research on street space and the visual elements of tourism sites. Then, we collected street view data of each intersection by traveling like tourists, corrected imaging parameters, and encoded the street view images. Second, based on the deep learning method (Fully Convolutional Networks, FCN), we segmented the collected street view images semantically and extracted the visual elements in street view data. Finally, by combining with GIS, we set up a geographic information database to analyze the visual and spatial characteristics of each sampling point's visual elements. This database was aimed at providing a basis for further evaluation of the visual quality of street space. It was aggregated using the street line as the smallest unit. In our study, we calculated the visual quality indicators to evaluate the street space in Gulangyu. The results show that: (1) There is obvious spatial differentiation in the visual elements of street space in Gulangyu; (2) Building density, street width, and vegetation sketches are the basic visual elements that shape the visual quality of street space; (3) The distribution of botanical parks, major docks, and commercial facilities significantly impacts the street space's visual quality. In detail, green plants, buildings, roads, sky, and street facilities show the differences between a center and a roundabout. While the distribution of pedestrians shows differences between the east and the west. The green view rate, enclosure, sky openness, and diversity of street space also have obvious center-roundabout spatial differentiation. Moreover, there is an obvious spatial agglomeration effect in the green view rate, crowding degree, and diversity of street space. The agglomeration points are mainly parks, docks, and commercial streets. The method in this paper provides a new collection method in street visual quality evaluation. The visual element extraction accuracy based on FCN is fairly high, which can provide a reference for street view images and other types of image data analysis. This paper provides a valuable reference for street space management and planning, resource integration and allocation, human flow guidance, and regulation in tourism sites.