Understanding how landscape composition relates to aesthetic perception remains a challenge in landscape design research. While semantic segmentation methods have achieved strong performance in scene understanding, they are rarely used to analyze design structure and aesthetic interpretation in garden landscapes. This study introduces a computational framework that integrates semantic segmentation with compositional design descriptors and social aesthetic indicators for the analysis of garden design language. A dataset containing more than 2,000 garden images was constructed with pixel-level semantic annotations and associated aesthetic labels. Based on the segmentation outputs, a set of quantitative descriptors was derived to represent spatial composition of landscape elements, including spatial distribution, proportional composition, and element adjacency relationships. These compositional descriptors were further combined with social aesthetic indicators obtained from public surveys and textual analysis to explore relationships between landscape structure and perceived aesthetic qualities. Experimental results show that the proposed framework achieves competitive performance in semantic segmentation while providing interpretable representations of landscape design composition. The analysis further reveals consistent associations between certain compositional patterns and aesthetic perception. The proposed approach demonstrates the potential of integrating semantic scene understanding with design-aware feature representation for data-driven analysis of landscape aesthetics and garden design language.
Wang et al. (Mon,) studied this question.