Assessment of Elderly-Oriented Spatial Visual Quality in Pocket Parks: A New Method Based on Panoramic Semantic Segmentation

Shengzhen Wu; Jingru Chen; Junjie Song; Chen Pan; Enlong Li

doi:10.5566/ias.4043

Authors

Shengzhen Wu Jimei University https://orcid.org/0009-0008-8322-1517
Jingru Chen
Junjie Song
Chen Pan
Enlong Li

DOI:

https://doi.org/10.5566/ias.4043

Keywords:

Deep learning, elderly-friendly design, panoramic image, pocket park, spatial visual quality

Abstract

As global population aging intensifies, pocket parks in high-density urban areas have emerged as vital venues for older adults' daily activities. However, existing research predominantly relies on roadway-captured street view imagery (SVI), which inadequately captures the internal spatial heterogeneity and micro-scale visual features of these parks. To address this, we evaluated the age-friendly spatial visual quality (SVQ) of 29 pocket parks on Xiamen Island from an internal panoramic perspective. We developed the Older-Adult Friendly Visual Quality Index (OAVQ-I) utilizing panoramic imagery and deep learning-based semantic segmentation to quantify six indicators: Green View Ratio (GVR), Natural Elements Richness (NER), Sky Openness (SO), Enclosure (Encl), Rest Facilities Visibility (RFV), and Walkable Space Ratio (WSR). Coefficient of variation (CV) objective weighting revealed that RFV, Encl, and SO exhibit the highest relative dispersion among the indicators, collectively comprising 87% of the total weight. Notably, a high GVR does not guarantee high quality; excessively dense vegetation can create an "ecological trap" by obstructing facilities and limiting light penetration. Comparative case analysis identified three distinct spatial patterns: high-quality friendly, moderately friendly, and low-quality (requiring improvement). This study advances urban green space evaluation from macroscopic statistical assessment to microscopic functional diagnosis. The findings suggest that urban renewal should transition from blindly increasing greenery to optimizing spatial quality—specifically by pruning mid-level vegetation to enhance facility visibility and illumination—thereby creating safer, more supportive environments for older adults.

Author Biography

Shengzhen Wu, Jimei University

Teacher of College of Arts and Design, Jimei University, Xiamen, China, 361000.
Main research fields include: urban planning theory, environmental design, artificial intelligence and innovative design, sustainable development and design innovation of cultural heritage, etc.

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