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Article

A Dual-Height AI Framework for Proxy Assessment of Children’s Spatial Perception in a Large Cultural Complex

1
School of Architecture, South China University of Technology, Guangzhou 510641, China
2
State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510641, China
*
Author to whom correspondence should be addressed.
Buildings 2026, 16(10), 2030; https://doi.org/10.3390/buildings16102030
Submission received: 20 April 2026 / Revised: 15 May 2026 / Accepted: 18 May 2026 / Published: 21 May 2026
(This article belongs to the Special Issue Data-Driven Intelligence for Sustainable Urban Renewal)

Abstract

Large-scale cultural complexes serve significant numbers of child users, yet existing spatial assessment approaches are predominantly developed from adult perspectives and rarely consider child-height environmental exposure conditions at children’s own eye level. To address this gap, this study introdus a novel dual-height proxy assessment framework that integrates semantic segmentation with explainable machine learning, enabling scalable proxy-based spatial diagnosis without requiring direct child participation. This study proposes a proxy-based assessment framework combining dual-height street-view imagery (adult: 1.6 m; child: 1.2 m), semantic segmentation (DeepLabV3+ and PSPNet), GIS analysis, literature-informed proxy perceptual indices, and explainable machine learning (XGBoost with SHAP) applied across 480 sampling locations at the Longgang Cultural Centre, Shenzhen. The results reveal substantial differences in environmental exposure characteristics between adult-height and child-height viewpoints, with child-height imagery exhibiting 34% lower signage visibility and 30% higher spatial enclosure. Exploratory associations between environmental features and proxy perceptual indices yielded R2values ranging from 0.14 to 0.39, with walking distance, openness, and visual complexity emerging as the most influential variables within the proxy models. SHAP analysis identified non-linear relationships between environmental characteristics and proxy perception-related outcomes, and spatial mismatch mapping identified 120 locations warranting design attention. The study proposes a scalable and data-driven spatial proxy assessment framework to support child-friendly environmental screening and spatial diagnosis. The proposed proxy indices are grounded in developmental psychology literature and are not intended to substitute for children’s direct perceptual responses; rather, they are intended to characterise comparative child-height environmental exposure patterns within large-scale cultural environments. Validation using child-reported perception data, behavioural observation, participatory methods, and experimental wayfinding studies remains an important direction for future research.
Keywords: children’s spatial perception; proxy assessment; semantic segmentation; dual-height imagery; child-friendly design children’s spatial perception; proxy assessment; semantic segmentation; dual-height imagery; child-friendly design

Share and Cite

MDPI and ACS Style

Shen, Y.; Zhu, S.; Zhang, F. A Dual-Height AI Framework for Proxy Assessment of Children’s Spatial Perception in a Large Cultural Complex. Buildings 2026, 16, 2030. https://doi.org/10.3390/buildings16102030

AMA Style

Shen Y, Zhu S, Zhang F. A Dual-Height AI Framework for Proxy Assessment of Children’s Spatial Perception in a Large Cultural Complex. Buildings. 2026; 16(10):2030. https://doi.org/10.3390/buildings16102030

Chicago/Turabian Style

Shen, Yingying, Shuyan Zhu, and Fei Zhang. 2026. "A Dual-Height AI Framework for Proxy Assessment of Children’s Spatial Perception in a Large Cultural Complex" Buildings 16, no. 10: 2030. https://doi.org/10.3390/buildings16102030

APA Style

Shen, Y., Zhu, S., & Zhang, F. (2026). A Dual-Height AI Framework for Proxy Assessment of Children’s Spatial Perception in a Large Cultural Complex. Buildings, 16(10), 2030. https://doi.org/10.3390/buildings16102030

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