Resumen
The physical presence of a street, called the ?street view?, is a medium through which people perceive the urban form. A street?s spatial ratio is the main feature of the street view, and its measurement and quality are the core issues in the field of urban design. The traditional method of studying urban aspect ratios is manual on-site observation, which is inefficient, incomplete and inaccurate, making it difficult to reveal overall patterns and influencing factors. Street view images (SVI) provide large-scale urban data that, combined with deep learning algorithms, allow for studying street spatial ratios from a broader space-time perspective. This approach can reveal an urban forms? aesthetics, spatial quality, and evolution process. However, current streetscape research mainly focuses on the creation and maintenance of spatial data infrastructure, street greening, street safety, urban vitality, etc. In this study, quantitative research of the Beijing street spatial ratio was carried out using street view images, a convolution neural network algorithm, and the classical street spatial ratio theory of urban morphology. Using the DenseNet model, the quantitative measurement of Beijing?s urban street location, street aspect ratio, and the street symmetry was realized. According to the model identification results, the law of the gradual transition of the street spatial ratio was depicted (from the open and balanced type to the canyon type and from the historical to the modern). Changes in the streets? spatiotemporal characteristics in the central area of Beijing were revealed. Based on this, the clustering and distribution phenomena of four street aspect ratio types in Beijing are discussed and the relationship between the street aspect ratio type and symmetry is summarized, selecting a typical lot for empirical research. The classical theory of street spatial proportion has limitations under the conditions of high-density development in modern cities, and the traditional urban morphology theory, combined with new technical methods such as streetscape images and deep learning algorithms, can provide new ideas for the study of urban space morphology.