A Framework of Full-Process Generation Design for Park Green Spaces
  Based on Remote Sensing Segmentation-GAN-Diffusion
Ran Chen Xingjian Yi Jing Zhao Yueheng He Bainian Chen Xueqi Yao Fangjun Liu Haoran Li Zeke Lian
The development of generative design driven by artificial intelligence
algorithms is speedy. There are two research gaps in the current research: 1)
Most studies only focus on the relationship between design elements and pay
little attention to the external information of the site; 2) GAN and other
traditional generative algorithms generate results with low resolution and
insufficient details. To address these two problems, we integrate GAN, Stable
diffusion multimodal large-scale image pre-training model to construct a
full-process park generative design method: 1) First, construct a
high-precision remote sensing object extraction system for automated extraction
of urban environmental information; 2) Secondly, use GAN to construct a park
design generation system based on the external environment, which can quickly
infer and generate design schemes from urban environmental information; 3)
Finally, introduce Stable Diffusion to optimize the design plan, fill in
details, and expand the resolution of the plan by 64 times. This method can
achieve a fully unmanned design automation workflow. The research results show
that: 1) The relationship between the inside and outside of the site will
affect the algorithm generation results. 2) Compared with traditional GAN
algorithms, Stable diffusion significantly improve the information richness of
the generated results.