Visual Engagement: Quantifying Campus Experiences in Urban Open Spaces Using a Computer Vision Model

dc.AffiliationOctober University for modern sciences and Arts MSA
dc.contributor.authorMohareb, Nabil
dc.contributor.authorAshraf, Abdelaziz
dc.date.accessioned2024-05-18T06:34:53Z
dc.date.available2024-05-18T06:34:53Z
dc.date.issued2024-05
dc.description.abstractIntroduction Addressing the gap in quantitative analysis of spatial experiences within academic environments, this study introduces a groundbreaking framework designed to measure and quantify the visual experiences of individuals in academic campus settings. Focused on analyzing the visual composition of the built environment—including aspects such as visible sky, greenery, and spatial enclosure—our framework aims to provide a quantitative refl ection of the subjective spatial experiences of campus users. Methods The methodology involves using mobile phones with digital cameras and GPS sensors to capture firstperson visual data and track movements as they freely traverse campus open spaces. Computer vision techniques, including Instance segmentation and convolutional neural networks, will categorize architectural and natural elements within each frame image extracted from a recorded video, quantify proportional compositions and analyze relative amounts of greenery, open sky, walkways, buildings, and other built structures that participants visually experienced. The framework is translated into a Python model capable of producing quantitative outcomes. The analysis will be further enriched by integrating Geographic Information Systems (GIS) for spatial analysis to identify navigation and visual engagement patterns. This comprehensive methodology quantifi es the visual attributes of spaces and interprets their impact on the behavior and experiences of campus users. Results and conclusions The study outcomes reveal relationships between student’s navigation choices, visual experiences, and scene types. The results aim to guide urban designers in understanding university students’ open space needs based on their natural movement and viewing preferences and complement other qualitative approaches.en_US
dc.description.urihttps://www.researchsquare.com/researchers/preprints
dc.identifier.doihttps://doi.org/10.21203/rs.3.rs-4339232/v1
dc.identifier.otherhttps://doi.org/10.21203/rs.3.rs-4339232/v1
dc.identifier.urihttp://repository.msa.edu.eg/xmlui/handle/123456789/5994
dc.language.isoenen_US
dc.relation.ispartofseriesResearch Square;
dc.subjectComputer Vision, instance Segmentation, Convolutional Neural Networks, Spatial Analysis, Navigation Behavior, University open spacesen_US
dc.titleVisual Engagement: Quantifying Campus Experiences in Urban Open Spaces Using a Computer Vision Modelen_US
dc.typeArticleen_US

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