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Data-efficient image transformer for landscape character classification and visual comfort prediction in Chinese hospitals
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DOI：10.3389/frai.2026.1733709.png)
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Abstract
En 中文
In hospital landscapes; where visual comfort influences stress recovery and patient satisfaction; reliable computational tools are needed to link landscape character with human perception. However; existing research on therapeutic landscapes in healthcare has largely focused on qualitative evaluations and design guidelines; with limited development of integrated; interpretable computational models that quantitatively connect landscape characteristics with human perception outcomes. This study addresses this gap by developing an AI-driven decision support system that integrates landscape character classification with visual comfort prediction in Chinese hospital settings. From 488 images collected across three hospitals; 30 representative images were evaluated by 252 respondents. Perception scores were assigned to all images based on landscape character; creating a labeled dataset. A Data-Efficient Image Transformer (DeiT) with dual prediction heads was developed for simultaneous landscape character classification and continuous visual comfort score regression. The model achieved a 96.34% classification accuracy and a mean absolute error (MAE) of 0.055 for visual comfort prediction; substantially outperforming ResNet-50 (accuracy: 89.39%; MAE: 0.148) and the standard Vision Transformer (ViT) (accuracy: 94.06%; MAE: 0.155). The DeiT model demonstrated 19–26% faster convergence and 62–65% improved visual comfort prediction. These results demonstrate that therapeutic landscape qualities; often regarded as subjective; exhibit consistent; computationally learnable patterns. The validated framework provides landscape architects; hospital planners; and administrators with an evidence-based tool for systematic therapeutic landscape evaluation and optimization.
Keywords:
vision transformer
attention mechanism
therapeutic landscape
visual comfort
data-efficient image transformer
hospital landscape
landscape character
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