
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.
Autor / Author: | Liu, Xun; Yang, Nandi; Tian, Runjia |
Institution / Institution: | University of Virginia, Virginia/USA; SWA Group, California/USA; Generative Game Inc., California/USA |
Seitenzahl / Pages: | 8 |
Sprache / Language: | Englisch |
Veröffentlichung / Publication: | JoDLA – Journal of Digital Landscape Architecture, 9-2024 |
Tagung / Conference: | Digital Landscape Architecture 2024 – New Trajectories in Computational Urban Landscapes and Ecology |
Veranstaltungsort, -datum / Venue, Date: | Vienna University of Technology, Austria 05-06-24 - 07-06-24 |
Schlüsselwörter (de): | |
Keywords (en): | Artificial intelligence, planting design, generative adversarial network |
Paper review type: | Full Paper Review |
DOI: | doi:10.14627/537752020 |
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