文献类型：期刊文献

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

作者：Feng, Lu[1];Sun, Yuting[1];Yu, Chenwen[1];Chen, Ran[2];Zhao, Jing[2]

机构：[1]East China Univ Sci & Technol, Sch Art Design & Media, Shanghai 200237, Peoples R China;[2]Beijing Forestry Univ, Sch Landscape Architecture, Beijing 100083, Peoples R China

年份：2025

卷号：14

期号：4

外文期刊名：LAND

收录：;WOS:【SSCI(收录号:WOS:001475708100001)】；

基金：This research was funded by the National Natural Science Foundation of China, grant number 52208041, and the Shanghai Philosophy and Social Science Planning Project, grant number 2021ECK002.

语种：英文

外文关键词：landscape architecture; machine learning; generative adversarial network; plantscape design; flower border

摘要：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.