Academic Journal
Measuring Built Environment Restorativeness and Uncovering Nonlinear Mechanisms via Deep Learning and Multi-Source Visual Perception Data: A Youth-Centered Study in Changsha.
| Τίτλος: | Measuring Built Environment Restorativeness and Uncovering Nonlinear Mechanisms via Deep Learning and Multi-Source Visual Perception Data: A Youth-Centered Study in Changsha. |
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| Συγγραφείς: | Huang, Zhihuan, Lin, Jinying, Zhang, Zhe, Wang, Yu |
| Πηγή: | Buildings (2075-5309); Jul2026, Vol. 16 Issue 13, p2510, 31p |
| Θεματικοί όροι: | Deep learning, Visual perception, Built environment, Urban planning, Environmental quality, Youth development, Cities & towns |
| Γεωγραφικοί όροι: | Changsha (Hunan Sheng, China), China |
| Περίληψη: | Contemporary buildings and urban spaces are increasingly expected to support psychological well-being—a quality often termed "restorativeness." Conventional approaches to quantifying restorativeness rely on subjective surveys or coarse green metrics, failing to capture how specific building morphologies and street-level visual configurations shape restorative experiences, particularly for stress-prone groups such as young adults. This study develops a deep-learning-driven framework linking building visual elements to youth-specific perceived restorativeness, using Changsha, China, as a testbed. The framework comprises three AI-powered modules: the TrueSkill algorithm trains a deep learning model to predict six dimensions of youth perception (e.g., beautiful, clean, safe) from pairwise comparisons of street view images; the Mask2Former architecture segments street-level imagery into 18 building and street attributes; and the XGBoost-SHAP pipeline uncovers nonlinear associations and threshold-like patterns between these attributes and the composite Built Environment Restorativeness Index (BERI). Results reveal three key insights: tree coverage shows a sustained positive association without saturation; building density exhibits a weakening association at high levels, suggesting possible saturation; and road proportion follows a bidirectional pattern, shifting from negative to positive beyond a certain range. Spatially, high BERI zones concentrate where ecological assets and diverse building functions co-occur, while youth perception exhibits systematic mismatches (e.g., "beautiful but not clean," "safe but not lively"), traceable to imbalances in building form, street furniture, and commercial mix. These findings advance AI-assisted evaluation of built environments by shifting from one-dimensional metrics to interpretable, design-relevant diagnostics, offering a replicable evidence base for crafting youth-responsive buildings and streets. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Measuring Built Environment Restorativeness and Uncovering Nonlinear Mechanisms via Deep Learning and Multi-Source Visual Perception Data: A Youth-Centered Study in Changsha. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Huang%2C+Zhihuan%22">Huang, Zhihuan</searchLink><br /><searchLink fieldCode="AR" term="%22Lin%2C+Jinying%22">Lin, Jinying</searchLink><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Zhe%22">Zhang, Zhe</searchLink><br /><searchLink fieldCode="AR" term="%22Wang%2C+Yu%22">Wang, Yu</searchLink> – Name: TitleSource Label: Source Group: Src Data: Buildings (2075-5309); Jul2026, Vol. 16 Issue 13, p2510, 31p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+perception%22">Visual perception</searchLink><br /><searchLink fieldCode="DE" term="%22Built+environment%22">Built environment</searchLink><br /><searchLink fieldCode="DE" term="%22Urban+planning%22">Urban planning</searchLink><br /><searchLink fieldCode="DE" term="%22Environmental+quality%22">Environmental quality</searchLink><br /><searchLink fieldCode="DE" term="%22Youth+development%22">Youth development</searchLink><br /><searchLink fieldCode="DE" term="%22Cities+%26+towns%22">Cities & towns</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Changsha+%28Hunan+Sheng%2C+China%29%22">Changsha (Hunan Sheng, China)</searchLink><br /><searchLink fieldCode="DE" term="%22China%22">China</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Contemporary buildings and urban spaces are increasingly expected to support psychological well-being—a quality often termed "restorativeness." Conventional approaches to quantifying restorativeness rely on subjective surveys or coarse green metrics, failing to capture how specific building morphologies and street-level visual configurations shape restorative experiences, particularly for stress-prone groups such as young adults. This study develops a deep-learning-driven framework linking building visual elements to youth-specific perceived restorativeness, using Changsha, China, as a testbed. The framework comprises three AI-powered modules: the TrueSkill algorithm trains a deep learning model to predict six dimensions of youth perception (e.g., beautiful, clean, safe) from pairwise comparisons of street view images; the Mask2Former architecture segments street-level imagery into 18 building and street attributes; and the XGBoost-SHAP pipeline uncovers nonlinear associations and threshold-like patterns between these attributes and the composite Built Environment Restorativeness Index (BERI). Results reveal three key insights: tree coverage shows a sustained positive association without saturation; building density exhibits a weakening association at high levels, suggesting possible saturation; and road proportion follows a bidirectional pattern, shifting from negative to positive beyond a certain range. Spatially, high BERI zones concentrate where ecological assets and diverse building functions co-occur, while youth perception exhibits systematic mismatches (e.g., "beautiful but not clean," "safe but not lively"), traceable to imbalances in building form, street furniture, and commercial mix. These findings advance AI-assisted evaluation of built environments by shifting from one-dimensional metrics to interpretable, design-relevant diagnostics, offering a replicable evidence base for crafting youth-responsive buildings and streets. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Buildings (2075-5309) is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/buildings16132510 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 31 StartPage: 2510 Subjects: – SubjectFull: Changsha (Hunan Sheng, China) Type: general – SubjectFull: China Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Visual perception Type: general – SubjectFull: Built environment Type: general – SubjectFull: Urban planning Type: general – SubjectFull: Environmental quality Type: general – SubjectFull: Youth development Type: general – SubjectFull: Cities & towns Type: general Titles: – TitleFull: Measuring Built Environment Restorativeness and Uncovering Nonlinear Mechanisms via Deep Learning and Multi-Source Visual Perception Data: A Youth-Centered Study in Changsha. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Huang, Zhihuan – PersonEntity: Name: NameFull: Lin, Jinying – PersonEntity: Name: NameFull: Zhang, Zhe – PersonEntity: Name: NameFull: Wang, Yu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 20755309 Numbering: – Type: volume Value: 16 – Type: issue Value: 13 Titles: – TitleFull: Buildings (2075-5309) Type: main |
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