Based on Kevin Lynch’s cognitive method of urban image and Weibo’s review data, this study constructs a research framework with three modules as the core: city image structure, city image types, and cultural service evaluation. First, the geospatial information carried by comments is analyzed by GIS to obtain the image structure of the city; second, the picture data in the comments are divided into image types and the type ratio is calculated by the image semantic segmentation method based on deep full convolution neural network. Finally, the text data in the comments are extracted from the semantic word frequency analysis to evaluate the cultural service perception index words of the city image and combined with the analysis of the city image structure and the city image type so as to obtain the integrated comprehensive perception of the city image. The research shows that the introduction of big data and deep learning methods into city image research can make up for the shortcomings of traditional research samples, expand the dimension and breadth of urban cognition, reveal the social, cultural, and functional characteristics of the city, and is an important supplement to the five-element model of city image depicting the material form of the city. In addition, the results of the empirical study, taking Zhongshan City as an example, have implications for the realistic urban spatial planning, urban landscape design, and tourism industry layout of Zhongshan.

Urban street trees are of great significance to the sustainable development of human settlements, and are key factors to enhance the service value of the urban environmental ecosystem. In this regard, it is necessary to improve and balance the benefit distribution of urban street trees for promoting the environmental quality of cities. In order to make clear the urban street tree benefits in a city, this paper presents the study on the ecosystem services from street trees in Shengjing Historical and Cultural Block (SHCB), Shenyang, China. By conducting a field survey on 1968 street trees and using the i-Tree model and the ENVI-met model to quantify the original data, this paper evaluated the ecosystem services distribution of eight streets and nine zones in the SHCB. The results showed that the co-creation annual ecological benefit and annual thermal comfort benefit of the street trees of SHCB were 163,965.62 and 233,533.48 dollars, respectively, totaling 397,499.10 dollars. It is obvious that the thermal comfort benefit is higher than the ecological benefit. Meanwhile, urban streets with high ecological benefit may not necessarily produce high thermal comfort benefit. Therefore, on the basis of ecological benefit analysis, the ecosystem services can be reflected more accurately by superimposing the thermal comfort benefit. The quantitative assessment system obtained in this study can provide a reference for future block planning and urban street tree allocation of SHCB and other regions in similar areas.

Urban streets are critical public spaces that significantly influence the thermal comfort of city dwellers. However, the issue of summer thermal discomfort in severely cold regions has been largely overlooked. This study focuses on Youth Street in Shenyang, a city in a severely cold region, to explore the impact of various street spaces and vegetation layouts on the thermal environment and comfort using ENVI-met modeling and correlation analysis. The study varied the aspect ratio (AR) of the street, street tree species, and plant spacing across 60 scenarios and simulated thermal comfort over a 10-h period on a typical summer day. Results show that air temperature (Ta), mean radiant temperature (Tmrt) and sky view factor (SVF) are positively correlated with physiologically equivalent temperature (PET). Street trees effectively reduce Ta, increase RH and lower wind speed (WS), but plant spacing has minimal impact on WS. Higher AR values lead to greater improvements in pedestrian thermal comfort. Specifically, the highest heat mitigation rate (HMR) is observed at low AR (9.87% at AR = 0.5 and 9.94% at AR = 1.0), while it is lower at high AR (8.16% at AR = 2.0). Conversely, larger plant spacing of street trees diminishes the effectiveness of thermal comfort improvements. The improvement effect of plant spacing is more pronounced in street spaces with smaller AR. In these spaces, closer plant spacing significantly enhances thermal comfort by providing more shade and reducing Ta and Tmrt. However, in street spaces with higher AR, overly dense plant configurations can reduce WS and limit the cooling effect of ventilation, thereby diminishing overall heat mitigation ability. Conclusions suggest that urban planners should consider both street space and vegetation layouts to optimize thermal comfort. For urban main streets in severely cold regions, an AR of 1:1 with deciduous broadleaf trees and hedges planted at 6 m spacing is recommended. In high-AR streets, dense plant configurations should be avoided. This study provides valuable insights for improving the thermal comfort and sustainable design of urban street spaces, supporting new construction and development in similar climate environments.

Knowing the spatial structure of traditional villages is required to promote and preserve these villages. These traditional villages are an essential part of China’s farming legacy and hold substantial historical and cultural significance. Therefore, this article analyzed 30 nationally recognized traditional villages in Liaoning Province, selected from the 6819 traditional villages in the province, as samples. These were divided into three types based on elevation: plain-type (below 200 m above sea level), hilly-type (200–500 m), and mountain-type (above 500 m) villages. Two villages of each type were selected for a total of six villages as the study objects; for these, quantitative comparative research on the spatial structure of these villages was carried out. The results of the study show that: (1) plain-type traditional villages are little affected by the terrain, the overall presentation of the surface space, the village traffic is well developed, able to form a commercial street as the core of the road interruptions in the head of the road more; (2) hilly-type traditional villages are influenced by mountains and water systems, forming a linear space with main roads as the core and crossroads, their core areas are more remote and lack space for public activities, and the villages rely on religious venues or the former residences of celebrities to attract tourists; (3) mountain-type villages are greatly influenced by the mountains, making it difficult to form a commercial area, the distribution of each natural town is relatively scattered and forms a point-like space, each point is developed with public space as the core, and there is a lack of characteristics within the village. The above quantitative characteristics are compared and three targeted conservation strategies for national-level traditional villages in Liaoning are proposed.

Bamboos, as imperative vegetations in Chinese traditional gardens, also significantly influenced the recently originated Neo-Chinese-style landscape in China, and their habitat ranges have been profoundly impacted by global climate warming. Current studies on the distribution dynamics of bamboo reveal existent gaps in assessing the suitable distribution area of Neo-Chinese-style landscapes. In this study, we calculated the habitat ranges of two widely distributed bamboo genera (Phyllostachys and Bambusa) based on the optimal MaxEnt model, predicted their future (2050s, 2070s and 2090s) distributions under different climate scenarios (SSP1-2.6 and SSP5-8.5), and assessed the suitable distribution area of the Neo-Chinese-style landscape according to the distribution union of two bamboo genera. The results showed that the optimal MaxEnt model exhibited high evaluation indices (AUC > 0.90) for the two bamboo genera. The habitat ranges of bamboo genera were significantly influenced by the minimum temperature of the coldest month and would expand northwardly in the future. The suitable distribution area of Neo-Chinese-style landscapes covered about 71.3% cities of China, which would expand 5.9%–8.7% of cities and 10%–18.7% of cities under the SSP1-2.6 climate scenario and the SSP5-8.5 climate scenario, respectively. The suitable distributions are mainly located in the southeast part of China. This study advanced our understanding of the restriction of bamboo to the distribution of the Neo-Chinese-style landscape and provided valuable insights and a scientific basis for landscape construction in different areas of China.

Urban street greening has a positive impact on the health of citizens and the urban environment. This study takes the representative streets in the main urban area of Zhongshan City, Guangdong Province as an example to explore urban street greening perception from the perspective of combining Green visual index (GVI) and Normalized difference vegetation index (NDVI). This study uses a deep learning based image semantic segmentation method to analyze Baidu Street View to calculate the GVI of the street, and uses GF-1 satellite data to calculate NDVI to compare and analyze the characteristics and correlation of GVI and NDVI of urban streets. The results show that: 1. The GVI of streets in the central urban area of Zhongshan varies from 8.06% to 36.00%, with Xingzhong Road in Shiqi District Street having the highest GVI; 2. The mean value of NDVI of each street shows different changes with the increase of buffer scale, and the mean value of NDVI has a strong scale sensitivity; 3. The highest Pearson correlation coefficient between GVI and 25m DNVI mean value was 0.862; 4. The GVI prediction model based on NDVI is: y=0.8249x+0.0181, R ² =0.7433. On this basis, the shortcomings of street landscape are analyzed and optimization suggestions are given, providing reference for urban street landscape evaluation, spatial optimization, and landscape improvement.