  In traditional human living environment landscape design, the establishment
of three-dimensional models is an essential step for designers to intuitively
present the spatial relationships of design elements, as well as a foundation
for conducting landscape analysis on the site. Rapidly and effectively
generating beautiful and realistic landscape spaces is a significant challenge
faced by designers. Although generative design has been widely applied in
related fields, they mostly generate three-dimensional models through the
restriction of indicator parameters. However, the elements of landscape design
are complex and have unique requirements, making it difficult to generate
designs from the perspective of indicator limitations. To address these issues,
this study proposes a park space generative design system based on deep
learning technology. This system generates design plans based on the
topological relationships of landscape elements, then vectorizes the plan
element information, and uses Grasshopper to generate three-dimensional models
while synchronously fine-tuning parameters, rapidly completing the entire
process from basic site conditions to model effect analysis. Experimental
results show that: (1) the system, with the aid of AI-assisted technology, can
rapidly generate space green space schemes that meet the designer's perspective
based on site conditions; (2) this study has vectorized and
three-dimensionalized various types of landscape design elements based on
semantic information; (3) the analysis and visualization module constructed in
this study can perform landscape analysis on the generated three-dimensional
models and produce node effect diagrams, allowing users to modify the design in
real time based on the effects, thus enhancing the system's interactivity.
