Data-efficient image transformer for landscape character classification and visual comfort prediction in Chinese hospitals

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.