Deep Learning-Driven Generative Layout Design Model for Sensory Garden Environment Design

The spatial layout design of sensory gardens involves the collaborative optimization of multiple design decisions. Traditional methods rely on manual experience and are inefficient in scheme exploration. This study proposes a diffusion generation model that integrates design grammar rules and conditional constraints to achieve end to end generation from land use conditions to complete layout schemes. A multi-scale spatial feature encoder is designed to capture the spatial skeleton and element combination patterns of the garden through global and local scale hierarchical coding and cross-scale attention fusion. A conditional embedding module is constructed, in which hard constraints such as the land use red line and setback distance are encoded as differentiable vectors, and soft constraints such as visual permeability and spatial enclosure are encoded as differentiable vectors. A graph convolutional network is used to explicitly model the adjacency, inclusion, and axis relationships among design elements, and the topological prior is injected into the denoising process. The U-Net backbone is improved, and a dual-channel attention mechanism and a progressive refinement strategy are introduced to enhance structural awareness and boundary accuracy. Experiments are carried out on a dataset containing 200 cases, and ten indicators such as hard constraint violation rate, Fréchet distance, and spatial enclosure are used for evaluation. The results show that the hard constraint violation rate of this model is 2.3%, which is 84% lower than that of the standard diffusion model. The Fréchet distance is 8.7, and the spatial enclosure is 59.3%. All ten indicators outperform the four baseline methods such as LayoutGAN and LayoutVAE. The ablation experiment confirms the independent contribution of each core component, and the cross shape generalization and small sample experiments verify the adaptability and data efficiency of the model, which provided an effective method support for generative landscape design.