Predicting and Optimizing Restorativeness in Campus Pedestrian Spaces based on Vision Using Machine Learning and Deep Learning

Kuntong Huang, Taiyang Wang, Xueshun Li, Ruinan Zhang () and Yu Dong ()
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Kuntong Huang: School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
Taiyang Wang: School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
Xueshun Li: School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
Ruinan Zhang: School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China
Yu Dong: School of Architecture and Design, Harbin Institute of Technology, Harbin 150001, China

Land, 2024, vol. 13, issue 8, 1-26

Abstract: Restoring campus pedestrian spaces is vital for enhancing college students’ mental well-being. This study objectively and thoroughly proposed a reference for the optimization of restorative campus pedestrian spaces that are conducive to the mental health of students. Eye-tracking technology was employed to examine gaze behaviors in these landscapes, while a Semantic Difference questionnaire identified key environmental factors influencing the restorative state. Additionally, this study validated the use of virtual reality (VR) technology for this research domain. Building height difference (HDB), tree height (HT), shrub area (AS), ground hue (HG), and ground texture (TG) correlated significantly with the restorative state (ΔS). VR simulations with various environmental parameters were utilized to elucidate the impact of these five factors on ΔS. Subsequently, machine learning models were developed and assessed using a genetic algorithm to refine the optimal restorative design range of campus pedestrian spaces. The results of this study are intended to help improve students’ attentional recovery and to provide methods and references for students to create more restorative campus environments designed to improve their mental health and academic performance.

Keywords: visual perception; restorative environments; campus pedestrian space; machine learning; virtual reality; optimization design; convolutional neural network (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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