Abstract
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.
Abstract (translated)
在传统的人类居住环境景观设计中，建立三维模型是设计师直觉地呈现设计元素的空间关系以及进行场地景观分析的基础。快速有效地生成美丽且逼真的景观空间是设计师面临的一个重要挑战。尽管在相关领域中应用了大量的生成设计，但它们主要是通过限制指示参数来生成三维模型。然而，景观设计的元素是复杂和独特的，使得从指标限制的角度生成设计具有困难。为解决这些问题，本研究提出了一个基于深度学习技术的公园空间生成设计系统。该系统根据景观元素的拓扑关系生成设计计划，然后对计划元素信息进行向量化，并使用Grasshopper生成三维模型，同时同步微调参数，快速完成整个过程，从基本场地条件到模型效果分析。实验结果表明：（1）在AI辅助技术的帮助下，系统可以快速生成满足设计师观点的景观空间方案；（2）本研究根据语义信息对各种景观设计元素进行了向量化和解构；（3）本研究中的分析和可视化模块可以对生成的三维模型进行场地分析，并生成节点效应图，使用户可以根据影响效果实时修改设计，从而提高系统的互动性。
URL
https://arxiv.org/abs/2404.16067
