Assessment of Elderly-Oriented Spatial Visual Quality in Pocket Parks: A New Method Based on Panoramic Semantic Segmentation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article 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Panoramic Semantic Segmentation Shengzhen Wu, Jingru Chen, Junjie Song, Chen Pan, Enlong Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9140060/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract With the intensification of global population aging, pocket parks in high-density urban areas have emerged as key venues for the daily activities of older adults. However, existing research predominantly relies on street view images (SVI) captured from vehicle lanes, which inadequately captures the spatial heterogeneity and micro-scale visual features within these parks. This study aims to assess the age-friendly spatial visual quality (SVQ) of pocket parks from a panoramic perspective. This study examines 29 pocket parks on Xiamen Island. An index for Older-Adult Friendly Visual Quality Index (OAVQ-I) was developed. Panoramic imagery combined with deep learning-based semantic segmentation was employed to quantitatively analyze six indicators: Green View Rate (GVR), Natural Elements Richness (NER), Sky Openness (SO), Enclosure (Encl), Rest Facilities Visibility (RFV) and Walkable Space Ratio (WSR). Regression analysis was subsequently applied to determine indicator weights. The results indicate that RFV, Encl, and SO are the decisive factors, collectively accounting for 87% of quality variation. Notably, a high green view ratio does not necessarily indicate high quality; excessively dense vegetation may create an "ecological trap" by obstructing facilities and limiting light penetration. Through comparative case analysis, three spatial patterns were identified: the High-quality friendly type, the Moderately friendly type, and the Improvement needed - low quality type. This study advances the evaluation of urban green spaces from macroscopic statistics toward microscopic functional diagnosis. The findings suggest that urban renewal should transition from increasing greenery to enhancing quality, with particular emphasis on pruning mid-level vegetation to improve facility visibility and lighting conditions, thereby creating more supportive environments for older adults. Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Deep learning panoramic image elderly-friendly design pocket park spatial visual quality Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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