Research on Campus Space Features and Visual Quality Based on Street View Images: A Case Study on the Chongshan Campus of Liaoning University

As the university campus is a place for learning, conducting scientific research, and communication, campus street spatial quality has an impact on its users. Therefore, refinement evaluations of campus spatial quality are essential for constructing high-quality campuses. In this study, machine lear...

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Bibliographic Details
Published in:Buildings (Basel) 2023-05, Vol.13 (5), p.1332
Main Authors: Meng, Yumeng, Li, Qingyu, Ji, Xiang, Yu, Yiqing, Yue, Dong, Gan, Mingqi, Wang, Siyu, Niu, Jianing, Fukuda, Hiroatsu
Format: Article
Language:English
Subjects:
Aesthetics
Algorithms
Built environment
campus street space
Case studies
Colleges & universities
Deep learning
Image quality
Image segmentation
Influence
Machine learning
Perception
Perceptions
physical features
Research methodology
Semantic segmentation
Semantics
Spatial discrimination
spatial perception prediction
Technical services
Urban planning
visual beautiful quality (VBQ)
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Summary:As the university campus is a place for learning, conducting scientific research, and communication, campus street spatial quality has an impact on its users. Therefore, refinement evaluations of campus spatial quality are essential for constructing high-quality campuses. In this study, machine learning was used to conduct semantic segmentation and spatial perception prediction on street view images. The physical features and perception quality of the surrounding areas of the Chongshan campus of Liaoning University were obtained. The study found that the visual beautiful quality (VBQ) of the student living area was the highest, and the VBQ of the teacher living area was the lowest when compared to the research and study area, student living area, sports area, and surrounding area. Greenness and openness had positive influences on VBQ, while enclosure had a negative influence. This study analyzed the influence mechanism operating between spatial physical features and VBQ. The results provide theoretical and technical support for campus space spatial quality construction and improvement.
ISSN:2075-5309
2075-5309
DOI:10.3390/buildings13051332