Bibliographic Details
| Title: |
Measuring Built Environment Restorativeness and Uncovering Nonlinear Mechanisms via Deep Learning and Multi-Source Visual Perception Data: A Youth-Centered Study in Changsha. |
| Authors: |
Huang, Zhihuan, Lin, Jinying, Zhang, Zhe, Wang, Yu |
| Source: |
Buildings (2075-5309); Jul2026, Vol. 16 Issue 13, p2510, 31p |
| Subject Terms: |
Deep learning, Visual perception, Built environment, Urban planning, Environmental quality, Youth development, Cities & towns |
| Geographic Terms: |
Changsha (Hunan Sheng, China), China |
| Abstract: |
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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| Database: |
Complementary Index |