A Generative AI Approach
TYPE: Research
YEAR: 2023
Collaborators: Nandi Yang (SWA)
Funding: Supported by Patrick T. Curran Fellowship
YEAR: 2023
Collaborators: Nandi Yang (SWA)
Funding: Supported by Patrick T. Curran Fellowship
In the field of landscape
architecture, the integration of ecological principles with aesthetic design is
a critical yet complex task. This research introduces an innovative AI-driven
tool designed to revolutionize planting design by embedding ecological
intelligence into the design process. This paper presents the methodology,
including data synthesis, AI model training with generative adversarial
networks (GANs), and integration into a practical design interface. The model
is trained on multifaceted input data such as topography, soil, and climate
data. The results indicate the AI model's effectiveness in generating planting
layouts that balance ecological accuracy with aesthetic appeal. Significantly,
this research extends the professional practice of landscape architecture.
Full article can be found:
https://gispoint.de/fileadmin/user_upload/paper_gis_open/DLA_2024/537752020.pdf
Full article can be found:
https://gispoint.de/fileadmin/user_upload/paper_gis_open/DLA_2024/537752020.pdf
Methodology Framework
Rhino/Grasshopper Interface. We prototyped our Interface called ECO GEN using HumanUI in Grasshopper
Form finding, generative design integration
Iterative Design Process and Final Plating Layout Design Visualization