Urban Perception Evaluation and Street Refinement Governance Supported by Street View Visual Elements Analysis

As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analy...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2024-10, Vol.16 (19), p.3661
Main Authors: Tang, Fengliang, Zeng, Peng, Wang, Lei, Zhang, Longhao, Xu, Weixing
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Built environment
Clustering
Data analysis
Datasets
Deep learning
Geospatial data
Heterogeneity
Image quality
Mental task performance
Neural networks
Perception
Perceptions
Regression analysis
Spatial heterogeneity
Statistical analysis
Statistical models
street view data
Streets
Urban areas
Urban environments
urban perception
Urban planning
Visual discrimination learning
Visual perception
Visual system
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Summary:As street imagery and big data techniques evolve, opportunities for refined urban governance emerge. This study delves into effective methods for urban perception evaluation and street refinement governance by using street view data and deep learning. Employing DeepLabV3+ and VGGNet models, we analyzed street view images from Nanshan District, Shenzhen, identifying critical factors that shape residents’ spatial perceptions, such as urban greenery, road quality, and infrastructure. The findings indicate that robust vegetation, well-maintained roads, and well-designed buildings significantly enhance positive perceptions, whereas detractors like fences reduce quality. Furthermore, Moran’s I statistical analysis and multi-scale geographically weighted regression (MGWR) models highlight spatial heterogeneity and the clustering of perceptions, underscoring the need for location-specific planning. The study also points out that complex street networks in accessible areas enhance living convenience and environmental satisfaction. This research shows that integrating street view data with deep learning provides valuable tools for urban planners and policymakers, aiding in the development of more precise and effective urban governance strategies to foster more livable, resilient, and responsive urban environments.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs16193661