Research on the quantification of historical street space based on image semantic segmentation

Yan, Lina; Chen, Yile; Zheng, Liang; Zhang, Yi; Zhu, Chun

doi:10.1117/12.2630998

Cited by 1 publication

(1 citation statement)

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“…To support this analysis, Liu et al [8] utilized semantically segmented street environmental features with a fully convolutional neural network (CNN), speci cally the Xception-71 CNN, pre-trained on the Cityscapes dataset comprising pixel-level annotated street scenes from 50 different cities, demonstrating favorable performance compared to alternative CNN architectures. Yan et al [9] employed an urban perception evaluation framework to complement this approach. This framework analyzes a vast collection of old city landscape street images, focusing on image semantic segmentation to categorize data based on landscape spatial elements.…”

Section: Evaluating Urban Environments: Semantic Segmentation and Com...mentioning

confidence: 99%

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

Mohareb,

Ashraf

2024

Preprint

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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 reflection of the subjective spatial experiences of campus users. Methods The methodology involves using mobile phones with digital cameras and GPS sensors to capture first-person 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 quantifies 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.

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Section: Evaluating Urban Environments: Semantic Segmentation and Com...mentioning

confidence: 99%

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

Mohareb,

Ashraf

2024

Preprint

View full text Add to dashboard Cite

show abstract

Research on the quantification of historical street space based on image semantic segmentation

Cited by 1 publication

References 1 publication

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

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

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