Quantitative Perception of Street Space Based on Deep Learning and Future Morphology Prediction Using AIGC Technology

Abstract Street space facilitates public life within communities, and advancements in artificial intelligence technology bring innovative approaches to assessing and forecasting street space perceptions. This study is centered on the Tsim Sha Tsui area of Hong Kong, as an illustrative case. Street view images and historical photographs are used as primary data sources, leveraging deep learning for semantic segmentation to measure the constituent elements of street space. A perceptual evaluation model is developed using regression analysis and time-series forecasting models to quantify and describe in detail the regional styles and their evolution throughout the historical development of urban streets. The findings indicate that architectural elements consistently account for an average of 42.3% historically, while the proportion of carriageways has significantly increased since the 1980s, rising by 1.8% annually. Although the rate of greening has risen, sky visibility has declined, leading to a decreased level of openness. Utilizing ordinary least squares regression in conjunction with long short-term memory time-series predictions, it is anticipated that the share of buildings and signage will reach 57.6% by 2050 (+15.3% compared with 2020), with the share of sky and greenery decreasing to 18.9%. Subsequently, by integrating the quantitative results and substituting relevant assumptions, projections regarding the future transformations of street styles are made. Artificial intelligence–generated content (AIGC) technology is applied to adjust weights, enabling the fusion and transition of various elemental styles. The Stable Diffusion model is employed to generate three images representing future styles (traffic city, green city, and consumer city), and the correlation between the quantitative indicators and style migration is validated. This study demonstrates that the methodology employed can effectively analyze the historical evolution of street space and forecast multiple future scenarios, thereby providing data support for the revitalization of high-density urban streets. On the one hand, it can inform planning decisions by establishing thresholds for elemental occupancy, and on the other hand, it can assist in the selection of various scenarios through stylized image generation. Implementing street space quantification techniques and forecasting future styles provide valuable support for designers and evaluators of urban street design schemes, providing crucial insights for the design of street space and architectural form.