Street quality plays a crucial role in promoting urban development. There is still no consensus on how to quantify human street quality perception on a large scale or explore the relationship between street quality and street composition elements. This study investigates a new approach for evaluating and comparing street quality perception and accessibility in Shanghai and Chengdu, two megacities with distinct geographic characteristics, using street-view images, deep learning, and space syntax. The result indicates significant differences in street quality perception between Shanghai and Chengdu. In Chengdu, there is a curvilinear distribution of the highest positive perceptions along the riverfront space and a radioactive spatial distribution of the highest negative perceptions along the ring road and main roads. Shanghai displays a fragmented cross-aggregation and polycentric distribution of the streets with the highest positive and negative perceptions. Thus, it is reasonable to hypothesize that street quality perception closely correlates with the urban planning and construction process of streets. Moreover, we used multiple linear regression to explain the relationship between street quality perception and street elements. The results show that buildings in Shanghai and trees, pavement, and grass in Chengdu were positively associated with positive perceptions. Walls in both Shanghai and Chengdu show a consistent positive correlation with negative perceptions and a consistent negative correlation with other positive perceptions, and are most likely to contribute to the perception of low street quality. Ceilings were positively associated with negative perceptions in Shanghai but are not the major street elements in Chengdu, while the grass is the opposite of the above results. Our research can provide a cost-effective and rapid solution for large-scale, highly detailed urban street quality perception assessments to inform human-scale urban planning.

Global warming, the urban heat island effect (UHI), and the risks of fossil fuel depletion necessitate a re-evaluation of traditional settlements that have been adapted to local climatic conditions, topography, and available resources, including materials and construction methods, through passive strategies to achieve thermal comfort. Although vernacular settlements have received considerable attention, few have examined and evaluated their streetscapes. This study investigates the impact of topographical features and architectural forms on insolation and ventilation conditions in traditional settlements in China’s southern subtropical climate. The aim is to explore traditional planning configurations of streetscapes at different altitudes to identify architectural forms and planning strategies that effectively improve outdoor users’ thermal comfort conditions. For this purpose, case studies are conducted on three traditional settlements in Lingnan; the Lingnan region has a typical subtropical climate in southern China. The chosen cases represent the main features of different topographical conditions, architectural forms, and climate zones in the Lingnan. We systematically simulated the insolation and ventilation in these settlements’ streetscapes on a monthly and quarterly basis and analyzed their sunlight hours, incident solar radiation, shading percentages, sky view factors (SVF), and wind speed. The findings show the following: (1) Specific terrains can affect streetscapes’ shading percentages and wind speed. The mountain settlement (With an average elevation of 600 m) is located on a southeast-facing slope (10° < slope < 20°). It receives an additional 10% of incident solar radiation compared to gentle terrain. (2) Compared to settlements located in coastal hills and mountainous, plain settlements have better shading and ventilation conditions in streetscapes. In terms of insolation, plain settlements have denser building configurations and narrower, elongated street corridors with a height-to-width ratio (H/W) = 1.9~5.5 (the height–width ratio value as street’s H/W (H = height, W = width); note that it is unitless), which can generate greater lower SVF (44.5%), and shading percentages (63.6%). Regarding ventilation, it is easier to create a “cool lane” (i) when the main street, oriented towards the dominant wind direction in summer, forms an angle <30° with it, (ii) when the primary street follows a NE–SW longitudinal orientation, while SE–NW horizontal streets intersect and weave through it, and (iii) with a H/W = 3~4 resulting in wind speeds of 2.9~4.0 m/s. (3) All the streetscapes have overshadowing occurring in winter; similarly, varying sizes of calm wind zones are created in summer. To alleviate these issues, widening the streetscapes along the buildings can permit solar penetration and natural ventilation. (4) In summer, installing shading devices along the horizontal plane of covered street corridors with a H/W = 1~4 and N–S longitudinal orientation can provide an additional shading of 3.6–22%.

This study proposes an optimization method based on Rough Set Theory (RST) and Particle Swarm Optimization–Support Vector Regression (PSO-SVR), aimed at enhancing the emotional dimension of outdoor micro-space (OMS) design, thereby improving users’ outdoor activity duration preferences and emotional experiences. OMS, as a key element in modern urban design, significantly enhances residents’ quality of life and promotes public health. Accurately understanding and predicting users’ emotional needs is the core challenge in optimizing OMS. In this study, the Kansei Engineering (KE) framework is applied, using fuzzy clustering to reduce the dimensionality of emotional descriptors, while RST is employed for attribute reduction to select five key design features that influence users’ emotions. Subsequently, the PSO-SVR model is applied to establish the nonlinear mapping relationship between these design features and users’ emotions, predicting the optimal configuration of OMS design. The results indicate that the optimized OMS design significantly enhances users’ intention to stay in the space, as reflected by higher ratings for emotional descriptors and increased preferences for longer outdoor activity duration, all exceeding the median score of the scale. Additionally, comparative analysis shows that the PSO-SVR model outperforms traditional methods (e.g., BPNN, RF, and SVR) in terms of accuracy and generalization for predictions. These findings demonstrate that the proposed method effectively improves the emotional performance of OMS design and offers a solid optimization framework along with practical guidance for future urban public space design. The innovative contribution of this study lies in the proposed data-driven optimization method that integrates machine learning and KE. This method not only offers a new theoretical perspective for OMS design but also establishes a scientific framework to accurately incorporate users’ emotional needs into the design process. The method contributes new knowledge to the field of urban design, promotes public health and well-being, and provides a solid foundation for future applications in different urban environments.

In the farming era, the ancestors of Miao moved to a mountainous area in Qiandongnan to avoid wars. When they started their settlement construction, people gave priority to how to deal appropriately with the great survival pressure they were facing. This paper uses the methods GIS spatial analysis, morphological index, and spatial syntax to explain the spatial characteristics of Miao traditional settlements from the perspective of both regional scale and individual settlement and explores the adaptation mechanism. The results show that (1) spatial distribution of settlements shows a tendency of agglomeration and significant spatial heterogeneity; the maximum kernel density is in the Leikaitai area, which is featured by an inclined T shape; (2) settlements are concentrated in areas mainly around Qingshui River and Duliu River, with an elevation of 5001000m, terrain relief of 1020m, and the slope of 515°; (3) the external boundary of settlement is mainly finger-shaped and buildings showed a large concentration of small distribution; and (4) settlements have generally formed an overall landscape pattern of mountain-water-field-forest-building, with the space center appearing inside the settlements and the road connecting the outside of the settlement. This paper summarizes the intrinsic relationship among settlements spatial characteristics, the natural environment, and the social and economic environment and concludes the internal morphological evolution of the settlement which has shifted from survival adaptability to active search for development. The results of this research can provide a valuable reference for traditional settlement protection, utilization, and sustainable development.

Wearable technology has become increasingly popular in recent years, offering a range of applications including health monitoring, personal assistance, and fitness tracking. Artificial intelligence (AI) has played a significant role in enhancing the capabilities of wearable devices, enabling intelligent perception, personalized recommendations, multimodal interaction, health monitoring, intelligent homes, and smart cities. This technical review paper presents an overview of the key technologies involved in wearable design and how AI has enabled innovative solutions in the field. Furthermore, this paper proposes novel wearable design innovations, which are supported by AI algorithms, such as deep learning and natural language processing.