Aiming at the current isolated, static protection method of traditional villages, a comprehensive evaluation system for the living protection of traditional villages has been constructed based on the land use function integration concept in “Production–Living–Ecology” (PLE). By combining the “horizontal” PLE coupling coordination analysis with the “vertical” correlation analysis of the elements at each layer, the comprehensive evaluation and quantitative analysis of six traditional villages of different types and grades in the Taihu Lake area are carried out to quantitatively reflect the interactive relationship and integration mechanism of PLE in traditional villages. The results show that: (1) The PLE development of traditional villages is a dynamic process. Even if the villages are close in the PLE score, they may be in different stages of PLE development and coupling coordination type. (2) The “living” function has the highest correlation with the coupling coordination degree of PLE, and it acts as the engine and bridge of benign interaction between the PLE. (3) Even if the national traditional villages have a favorable ecology background, they may not get high scores, or even fail in the PLE score. (4) Among the sub-indicators, the natural environmental characteristics, the ecological vitality of political organizations, and the level of human settlement facilities show a significant linear correlation with the PLE score. Additionally, the ecological vitality of political organizations is the strongest. It can be therefore concluded that a positive policy organization is an important guarantee for realizing the PLE integration of traditional villages.

With the rapid development of information and sensory technology, the construction mode of universities and the planning of campus public spaces are confronting great challenges and opportunities. It also brings about new perspectives for reconsidering the relationship between users’ perceptions and the campus environment. This paper reviews the research on the perception of university public spaces over the past 20 years and summarizes the research hotspots by using co-citation analysis, co-occurrence analysis, and burst detection analysis through CiteSpace software. The results demonstrate that the overall development of this field experienced three stages: the initial development stage (2000–2007), the rapid growth stage (2008–2017), and the stable development stage (2018–2021). In terms of research content, hotspot studies are emphasized from the perspectives of thermal perceptions, health impact perception, spatial configuration perception, and user activity perception of on-campus space. In addition, this literature review concluded the emerging research tendencies and new quantification methods in recent years, proposing an enormous potential for quantifying campus space research based on new perceptual technologies. It also encourages the research and optimal design of campus spaces for a more student-oriented campus environment based on the study of the student’s perception of the spaces.

The mental health of university students has received much attention due to the various pressures of studies, life, and employment. Several studies have confirmed that campus public spaces contain multiple restorative potentials. Yet, the campus public space is still not ready to meet students’ new need for restorative percetions. Renewal practices for campus public spaces that integrate multi-issues are becoming more important, and further clarification of the measurement methods and optimization pathways is also needed. This study applied the semantic segmentation technique of the deep learning model to extract the feature indicators of outdoor public space based on street view image (SVI) data. The subjective evaluation of small-scale SVIs was obtained using the perceived restorative scale-11 (PRS-11) questionnaire. On this basis, restorative benefit evaluation models were established, including the explanatory and predictive models. The explanatory model used Pearson’s correlation and multiple linear regression analysis to identify the key indicators affecting restorative benefits, and the predictive model used the XGBoost 1.7.3 algorithm to predict the restorative benefit scores on the campus scale. The accessibility results from sDNA were then overlayed to form a comprehensive assessment matrix of restoration benefits and accessibility dimensions to identify further “areas with optimization potential”. In this way, three types of spatial dimensions (LRB-HA, HRB-LA, and LRB-LA) and sequential orders of temporal dimensions (short-term, medium-term, and long-term) were combined to propose optimization pathways for campus public space with the dual control of restorative benefits and accessibility. This study provides methodological guidelines and empirical data for campus regeneration and promotes outdoor public space efficiency. In addition, it can offer positive references for neighborhood-scale urban design and sustainable development.

As the focus on higher education in China gradually shifts from rapid development to an emphasis on quality, the need for campus environments to become facilitators of education has gained increasing attention. The accelerated development of information technology has also led to tremendous changes in both teaching and learning methods, with informal learning taking on an increasingly important role. Furthermore, the development of human sensing technology, especially visual perception technology, has brought in new opportunities for the research and optimization of informal learning spaces (ILSs) in universities. This paper focuses on the ILS in Chinese universities by exploring optimal design approaches based on visual perception analysis. Through research and field investigation, this paper proposes revised theoretical research of classifications and spatial elements of ILS in universities more applicable to the architectural study of space. This paper also explores practical optimal design methods with two case studies and makes experiments with wearable eye trackers to study the users’ perception in these spaces before and after optimization. The optimal design is made from the aspects of physical space, facilities, and environment. Visual perception experiments and quantitative analysis were used to obtain a higher level of experimental accuracy than the previous studies and thus to study the real feeling of users in spaces. By these means, the effect of the optimized design was verified and the relation between users’ perceptions and the spatial environments was explored for further improvements to optimal design methods. This article can provide theoretical and practical references for campus space optimization research and design, especially for ILS on university campuses.

In the context of climate change, surface urban heat islands (SUHIs) have become critical factors affecting the quality of the urban built environment. However, low-precision satellite thermal infrared remote sensing is suitable for urban scales but is insufficient to reveal the spatiotemporal distribution roles of surface heat islands at the neighborhood scale. This research takes the Sipailou Campus of Southeast University as an example and employs UAV thermal infrared imaging to acquire high-precision surface temperature data. It then systematically investigates the relationship and association mechanism between the surface urban heat island intensity (SUHII) and campus 2D/3D landscape configuration. The results indicate that the campus has a cooling effect during the daytime, with an average SUHII of −0.90 °C. It demonstrates the SUHII characteristics for campus land use types are as follows: SUHII_BD > SUHII_IS > SUHII_GS > SUHII_WB. Furthermore, the campus landscape has a significant hierarchical driving effect on SUHII, with the configuration of campus buildings and the impervious surface driving the strong heat island (SHI) and the 3D configuration and structure of greenspace dominantly strengthening the strong cool island (SCI). The overall design strategy of “two-dimensional priority, three-dimensional optimization” enables us to effectively mitigate the campus SUHII. This study reveals the spatiotemporal distribution characteristics of campus SUHII and the key influencing factors, and it also broadens the application of UAV thermal infrared imaging technology in the meso–micro-scale urban heat island assessment, providing suggestions for constructing a climate-adaptive urban landscape.