资源环境科技发展态势分析平台

Although it is widely known that quality of street space plays a vital role in promoting urban vibrancy, there is still no consensus on how to quantitatively measure it for a large scale. Recent emerging dataset Street View Picture has revealed the possibility to overcome the previous limit, thus bringing forward a research paradigm shift. Taking this advantage, this paper explores a new approach for visual quality evaluation and variation identification of street space for a large area. Hutongs, which typically represent for historical street space in Beijing, are selected for empirical study. In the experimental part, we capture multi-years Tencent Street View Picture covering all the Hutongs, and conduct both physical and perceived visual quality evaluation. The physical visual quality of street space is achieved automatically by combining 3-dimensional composition calculation of greenery, openness, enclosure using machine-learning segmentation method SegNet, and 2-dimensional analysis of street wall continuity and cross-sectional proportion; perceived visual quality of street space is evaluated by stay willingness scoring from five aspects. The variation of quality is evaluated based on the identified physical space variations. The result indicates that visual quality of Hutongs are not satisfied, while some regeneration projects in the historical protection block is better. Most Hutongs are in shortage of visual green, relative more continuous but with low cross-sectional ratio. Hutongs near main road witness an increasing trend of motorization. The difference between physical and perceived quality indicates the feasibility and limitation of the auto-calculation method. In the most recent 3-4 years, less than 2.5% Hutongs are improved, which are mainly slow beautification.