Visual Engagement: Quantifying Campus Experiences in Urban Open Spaces Using a Computer Vision Model
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Date
2024-05
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Article
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Research Square;
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Abstract
Introduction
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.
Description
Keywords
Computer Vision, instance Segmentation, Convolutional Neural Networks, Spatial Analysis, Navigation Behavior, University open spaces