The public’s mental health is obviously impacted by the perception of green quantity in urban streets. As one of the important urban spatial indicators, the Green View Index (GVI) reflects the green quantity of streets, which is helpful in revealing the level of street vegetation from the perspective of pedestrians. The GVI can improve the attraction and the visual experience in urban streets. Taking Qingdao Coastal Streets as an example, the study used OpenStreetMap, Baidu Street View (BSV) image, DeepLabV3+ semantic segmentation, and the SD method to obtain the GVI and Visual Comfort (VICO), and the correlation and influence mechanisms were discussed. The result showed that the greening landscape of the overall Qingdao Coastal Streets was of high quality, and the historic district was the most outstanding. The greening quality was a little low in the transitional district and the western modern district, which should be improved. In addition, the relationship between GVI and VICO showed a strong positive correlation. The spatial distribution of the VICO was more consistent with the GVI. The street VICO was affected by the GVI, plant richness, the street scale, and landscape diversity. Moreover, with the increase of the GVI, the increase trend of the VICO instead gradually decreased. The contribution of this study was not only accurately diagnosing the problems of street greening quality, shedding light on the relationship between GVI and VICO, but also providing theoretical support for urban greening planning and management, especially for healthy street design.

The coastal streets are the most attractive urban space, improving spatial quality and public perception of coastal streets is an important work of urban regeneration. The study used machine learning semantic segmentation, GIS and Semantic difference (SD) etc methods to obtain the spatial data and perceptual evaluation of coastal streets in Qingdao. Each of the six perceptual features, imageability, enclosure, human scale, transparency, complexity and nature, was taken as dependent variables and the corresponding physical features was taken as independent variables. The six regression models were established and the influence rules of spatial parameters on public perception were obtained. Meanwhile, based on the results of perceptual features evaluation, the overall coastal streets are divided into three types, open streets, mixed streets and biophilic streets. In all the three types coastal streets, the nature was the most significant perceptual feature due to the high greenness; the complexity was the lowest perceptual feature because of the low landscape diversity. The research results provided theoretical and technical support for the urban regeneration and spatial quality improvement of coastal streets in Qingdao.

Since the reform and opening-up policy, the accelerated urbanization rate has triggered extensive construction of new towns, leading to architectural homogenization and environmental quality degradation. As urban development transitions toward a “quality improvement” paradigm, there is an urgent need to synergistically enhance the health performance of human settlements through the optimization of public space environments. The purpose of this study is to explore the impact of the built environment of urban streets on residents’ perceptions. In particular, in the context of rapid urbanization, how to improve the mental health and quality of life of residents by improving the street environment. Changbai Island Street in the Heping District of Shenyang City was selected for the study. Baidu Street View images combined with machine learning were employed to quantify physical characterizations like street plants and buildings. The ‘Place Pulse 2.0’ dataset was utilized to obtain data on residents’ perceptions of streets as beautiful, safe, boring, and lively. Correlation and regression analyses were used to reveal the relationship between physical characteristics such as green visual index, openness, and pedestrians. It was discovered that the green visual index had a positive effect on perceptions of it being beautiful and safe, while openness and building enclosure factors influenced perceptions of it being lively or boring. This study provides empirical data support for urban planning, emphasizing the need to focus on integrating environmental greenery, a sense of spatial enclosure, and traffic mobility in street design. Optimization strategies such as increasing green coverage, controlling building density, optimizing pedestrian space, and enhancing the sense of street enclosure were proposed. The results of the study not only help to understand the relationship between the built environment of streets and residents’ perceptions but also provide a theoretical basis and practical guidance for urban space design.

As the university campus is a place for learning, conducting scientific research, and communication, campus street spatial quality has an impact on its users. Therefore, refinement evaluations of campus spatial quality are essential for constructing high-quality campuses. In this study, machine learning was used to conduct semantic segmentation and spatial perception prediction on street view images. The physical features and perception quality of the surrounding areas of the Chongshan campus of Liaoning University were obtained. The study found that the visual beautiful quality (VBQ) of the student living area was the highest, and the VBQ of the teacher living area was the lowest when compared to the research and study area, student living area, sports area, and surrounding area. Greenness and openness had positive influences on VBQ, while enclosure had a negative influence. This study analyzed the influence mechanism operating between spatial physical features and VBQ. The results provide theoretical and technical support for campus space spatial quality construction and improvement.

Urban parks are an important component of public urban spaces, which directly impact the living experiences of residents and the urban image. High-quality urban parks are crucial for enhancing the well-being of residents. This study selected Fukuoka, Japan, as the study site. Five urban parks were chosen to evaluate landscape visual quality by using the Scenic Beauty Estimation (SBE) method. The Semantic Differential (SD) method was used to get sample subjective landscape features. Meanwhile, sample objective landscape features were obtained by using semantic segmentation techniques in deep learning and combined with spatial analysis to understand their distribution. A regression model was established, which used the SBE values as the dependent variable and subjective landscape features as the independent variables to analyze the relationship between urban park landscape visual quality and subjective landscape features. The regression analysis revealed that sense of layering, harmony, interestingness, sense of order, and vitality were the core factors influencing visual quality. All five features had a significant positive impact on landscape visual quality. The sense of order was the most influential factor, which would be the key to enhancing the landscape perception experience. Moreover, the XGBoost model and SHAP value from machine learning were used to reveal the nonlinear relationships and significant threshold effects between urban park visual quality and five objective landscape features: openness, greenness, enclosure, vegetation diversity, and Shannon–Wiener diversity index. This study showed that when openness exceeded 0.27, the positive effect was significant. The optimal threshold for the greenness was 0.38. Vegetation diversity and enclosure had to be below 0.82 and 0.58, respectively, to have a positive impact. Meanwhile, the positive influence of the Shannon–Wiener diversity index reached its maximum at a value of 1.37. This study not only establishes a systematic method for diagnosing landscape problems and evaluating landscape visual quality but also provides both theoretical support and practical guidance for urban park landscape optimization.

Urban green spaces play a crucial role in providing social services and enhancing residents’ mental health. It is essential for sustainable urban planning to explore the relationship between urban green spaces and human perceptions, particularly their visual comfort. However, most current research has analyzed green spaces using two-dimensional indicators (remote sensing), which often overlook human visual perceptions. This study combined two-dimensional and three-dimensional methods to evaluate urban green spaces. Additionally, the study employed machine learning to quantify residents’ visual comfort in green-space environments and explored the relationship between green spaces and human visual perceptions. The results indicated that Kitakyushu exhibited a moderate FCV and an extremely low Green View Index (GVI). Yahatanishi-ku was characterized as having the highest visual comfort. Tobata-ku demonstrated the lowest visual comfort. Natural, GVI, openness, enclosure, vegetation diversity, landscape diversity, and NDBI were positively correlated with visual comfort. FCV and ENVI were negatively correlated with visual comfort. Vegetation diversity had the most impact on improving visual comfort. By integrating remote sensing and street-view data, this study introduces a methodology to ensure a more holistic assessment of green spaces. Urban planners could use it to better identify areas with insufficient green space or areas that require improvement in terms of green-space quality. Meanwhile, it could be helpful in providing valuable input for formulating more effective green-space policies and improving overall urban environmental quality. The study provides a scientific foundation for urban planners to improve the planning and construction of healthy and sustainable cities.