Deep Learning-Driven Garden Design: Integrating AI Into the Creative Process

The integration of artificial intelligence into landscape architecture has opened new frontiers for computational design, enabling the generation of garden and outdoor spatial layouts that blend ecological logic with aesthetic innovation. This study presents a novel AI-driven framework tailored for garden design, synergizing symbolic formalism with neural generative modeling to support the synthesis of complex, context-aware landscape artifacts. Traditional garden design often relies on fixed templates or heuristic rules that limit adaptability to environmental constraints and stylistic variations. These methods struggle with semantic coherence and lack responsiveness to evolving cultural and ecological aesthetics. To overcome these challenges, we propose a dual-component system comprising StyleMorphNet and the Semantic Stylization Protocol (SSP). StyleMorphNet is a multi-branch neural architecture that decomposes the generative process into latent subspaces covering spatial structure, material composition, and stylistic innovation. This modular design supports fine-grained control over layout generation while preserving landscape semantics. Complementing it, SSP embeds domain-specific knowledge—including seasonal dynamics, functional zoning, and regional styles—into the generative loop via rule-conditioned modulation and symbolic-visual reasoning. Empirical evaluations show that our framework produces garden designs with superior spatial harmony, stylistic diversity, and ecological relevance compared to existing models. This work contributes to the field of computational landscape design by providing an adaptive and semantically grounded framework that supports the co-creation of expressive, sustainable outdoor environments.