The Applicability of Two Generative Adversarial Networks to Generative Plantscape Design: A Comparative Study

Lu Feng, Yuting Sun, Chenwen Yu, Ran Chen and Jing Zhao ()
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Lu Feng: School of Art Design and Media, East China University of Science and Technology, Shanghai 200237, China
Yuting Sun: School of Art Design and Media, East China University of Science and Technology, Shanghai 200237, China
Chenwen Yu: School of Art Design and Media, East China University of Science and Technology, Shanghai 200237, China
Ran Chen: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Jing Zhao: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China

Land, 2025, vol. 14, issue 4, 1-23

Abstract: Plantscape design combines both scientific and technical elements, with flower borders serving as a representative example. Generative Adversarial Networks (GANs), which can automatically generate images through training, offer new technological support for plantscape design, potentially enhancing the efficiency of designers. This study focuses on flower border plans as the research subject and creates a dataset of flower border designs. Subsequently, the research employed two algorithms, Pix2Pix and CycleGAN, for training and testing, enabling the automatic generation of flower border design images, with subsequent optimization of the results. The paper compares the generated results of both algorithms in terms of image quality and design patterns, providing both objective and subjective evaluations of CycleGAN, which performed better. Experimental results show that the algorithm can learn the latent patterns of flower border design to some extent and generate high-quality images with reasonable performance in terms of ornamental character and ecological character. Among the design types, bar-shaped layouts showed the best results. However, the algorithm still faces challenges in handling complex site processing, boundary clarity, and design innovation. Additionally, aspects such as vertical variation, texture harmony, low maintenance, and sustainability remain areas for future improvement. This study demonstrates the potential of GAN in small-scale plantscape design and offers innovative and feasible solutions for flower border design.

Keywords: landscape architecture; machine learning; generative adversarial network; plantscape design; flower border (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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