Exploring spatial visual characteristics of scenic archetypes through AI multimodal mapping methods in Hangzhou Westlake

Traditional Chinese gardens embody sophisticated spatial design principles often described through abstract terms like “scenic archetypes,” yet systematic methods for analyzing their visual spatial characteristics remain underdeveloped. This study establishes an analytical framework integrating phenomenological theory with AI-enabled multimodal mapping to quantify spatial visual characteristics of four scenic archetypes, including framed, obstructive, porous, and sandwiched scenery, at Hangzhou West Lake. By decomposing scenic compositions and configurations into foreground-middle-background hierarchies characterized through shape, size, position, and texture variables, the framework achieves 94.12% classification accuracy via random forest modeling while revealing each archetype. Statistical analysis identifies archetype-specific spatial strategies: framed scenery employs regular foreground geometry with smooth depth transitions; obstructive scenery utilizes systematic positioning with texture contrasts; porous scenery balances visual permeability with textural variation; sandwiched scenery creates bilateral symmetry with channeling effects. This approach provides replicable methodology for heritage conservation and contemporary landscape design informed by traditional spatial wisdom.