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.

Discovering the Representative places (RPs) of a city will benefit the understanding of local culture and help to improve life experiences. Previous studies have been limited in regard to the large-scale spatial identification of RPs due to the vagueness of boundaries and the lack of appropriate data sources and efficient tools. Furthermore, human perception of these places remains unclear. To address this gap, this research adopts a novel approach to identify and evaluate the RPs of a city from the perspective of human perception. Our methodology involves the utilization of deep learning systems, text semantic analysis, and other techniques to integrate multi-source data, including points of interest (POIs), street view images, and social media data. Taking Nanjing, China, as a case, we identified 192 RPs and their perceptual ranges (PRRPs). The results show the following: (1) Comparing RPs to non-RPs, RPs show higher average scores across four perceptual dimensions (positive indicators): Beautiful (7.11% higher), Lively (34.23% higher), Safety (28.42% higher), and Wealthy (28.26% higher). Conversely, RPs exhibit lower average scores in two perceptual dimensions (negative indicators): Boring (79.04% lower) and Depressing (20.35% lower). (2) Across various perceptual dimensions, RPs have utilized 15.13% of the land area to effectively cover approximately 50% of human perceptual hotspots and cold spots. (3) The RPs exhibit significant variations across different types, levels, and human preferences. These results demonstrate the positive perceived effects that RPs have, providing valuable insights to support urban management, the transformation of the built environment, and the promotion of sustainable urban development, and provide guidance for urban planners and designers to make improvements in urban design and planning to make these sites more attractive.