摘要
【目的】植物景观设计需要科学性和技术性兼备。探索人工智能,特别是生成对抗网络(generative adversarial network,GAN)在植物景观设计中的应用,能够帮助设计师提高设计过程的效率。【方法】以花境平面图生成设计为例,建立了基于细致筛选优化的植物平面数据集。数据集标注基于植物分类,考虑了植物的种类、搭配原则及空间布局规律。引入循环生成对抗网络(cycle generative adversarial network,CycleGAN)模型对数据集进行学习,实现花境平面设计的自动生成。【结果】CycleGAN模型在以花境为代表的植物景观设计中具有独特的优势,花境平面图生成模型能够准确识别条形场地边界,并在色彩再现方面表现出较高的精度和可识别性。生成平面图的空间布局中,色块大小、平面布局形态和位置展示了各种植物的空间分布特点,并能够复现部分潜在搭配组合,生成了符合美学和生态原则的设计方案。然而,模型在部分场地边框的准确识别和设计结果的多样性方面仍存在局限。【结论】证明了CycleGAN在植物景观设计领域的应用潜力,并为实践中的植物景观设计提供了创新和有效的解决方案。
[Objective]Plantscape design is an interdisciplinary field blending art and science.Designers often face multiple challenges in the practical application of plantscape design:1)Difficulty in summarizing design principles;2)barriers for beginners amidst a high demand for and shortage of designers;3)low efficiency in revisions;4)prevalent manual labor,presenting significant opportunities for automation.The design of flower borders,representing a significant aspect of plantscape design,is suitable for case studies.In this context,machine learning offers new technical support.For patterns that are difficult to summarize,machine learning can rapidly process large volumes of experiential data through probability density estimation,thus reducing reliance on designers'intuition while significantly enhancing design efficiency and reducing labor intensity.Previous research confirmed the effectiveness of generative adversarial network(GAN)in layout generation.Research on plantscape generation based on GAN endeavors to rapidly learn from a large dataset of plant combinations with inherent patterns to generate high-quality plant layout designs that meet real-world application needs,thus assisting designers in quickly producing plant layouts at the initial design stages.Further integration of image processing and evaluative feedback can generate plant arrangement schemes that are both standard and artistic.GAN can provide exploratory tools and methods for the landscape design field,promoting innovation and development in the generation of plant arrangement scheme.[Methods]The research proposes an experimental framework for a plantscape plan generation model based on GAN.The research design mainly includes three steps:Dataset preparation,model training,and model assessment.Initially,high-quality flower border plan images are collected from a single design firm,and these images are statistically categorized based on spatial structure types and design elements,thereby establishing principles for sample selection to ensure the professionalism and scientificity of the dataset adopted.Then,the selected flower border images undergo preprocessing,including plant classification,merging,image resizing,color tagging,and data augmentation.The cycle generative adversarial network(CycleGAN)algorithm is applied to build the flower border plantscape plan generation model on an open-source programming platform,undergoing multiple training rounds.Lastly,the research objectively analyzes and compares the spatial results of the generated flower border plans with actual design plans.In terms of subjective evaluations,expert scoring and other feedback methods are adopted to assess the generated plantscape images in terms of aesthetics and ecology.[Results]The objective evaluation reveals that:1)The model most accurately identifies the contours of rectangular sites,requiring additional optimization for certain curved sites;2)The model can accurately learn and reflect about half of the tagged types in the dataset,with most generated color blocks closely matching the pre-determined colors in the dataset,demonstrating high color accuracy;3)The model needs to enhance the diversity of its outputs when handling similar sites.In terms of spatial structure generation,1)the unit area of generated results conforms to the application and volume characteristics of plants typically used in flower borders;2)The results can replicate plant combinations frequently used in the dateset;3)The results can learn the spatial distribution patterns of plants,replicating the shapes of edge plants,internal plant patches,and sculptural shrubs;4)The results can display vertical variation.In subjective evaluations,rectangular flower borders show certain advantages in aesthetics and ecology,particularly in low maintenance and sustainability,but there is room for improvement in seasonal variation and texture coordination.Curved flower borders are slightly inferior in aesthetics but perform reasonably well in ecological coordination,needing deeper optimization in plant diversity and aesthetics.[Conclusion]Cycle GAN exhibits unique advantages in plantscape design represented by flower borders.Although the number of training samples is not extensive,the Cycle GAN model performs well in expressing image quality and spatial layout,and demonstrates accurate boundary recognition for rectangular sites and high precision in color reproduction.The generated plan images mimic the dataset well.The spatial layout of the generated plans showcases the spatial distribution characteristics and visual effects of various plants,reproducing some potential combination patterns.The generated flower border designs align with actual design samples in terms of color,seasonal change,layering,and harmony.Ecologically,the generated designs embody sustainability principles,emphasizing the convenience of sustainable maintenance and management.However,the application of Cycle GAN also demonstrates certain limitations.First,the aesthetic and ecological quality of GAN-generated flower border designs highly depends on the quality and diversity of the training dataset.Secondly,Cycle GAN models tend to produce generic designs,lacking diversity in similar sites.In view of this,future research may focus on improving model algorithms,enriching the training dataset,and investigating the potential of introducing conditional generative adversarial network(CGAN).
作者
冯璐
余辰雯
孙雨婷
赵晶
FENG Lu;YU Chenwen;SUN Yuting;ZHAO Jing(the School of Art Design and Media,East China University of Science and Technology;the Key Laboratory of Ecology and Energy Saving Study of Dense Habitat,Ministry of Education;the School of Landscape Architecture,Beijing Forestry University;Beijing Laboratory of Urban and Rural Ecological Environment)
出处
《风景园林》
北大核心
2024年第9期59-68,共10页
Landscape Architecture
基金
国家自然科学基金“基于生成对抗网络的公园布局生成设计与结果评价方法”(编号52208041)
上海市哲学社会科学规划项目“韧性城市下基于社会-生态视角的长三角绿色基础设施优化研究”(编号2021ECK002)
高密度人居环境生态与节能教育部重点实验室课题“基于生成对抗网络的‘公园绿地设计方案快速生成’人机协同方法研究”(编号20220110)
北京高校高精尖学科建设项目“城乡人居生态环境学”。
关键词
风景园林
植物景观设计
机器学习
神经网络
循环生成对抗网络
花境
landscape architecture
plantscape design
machine learning
neural network
cycle generative adversarial network(CycleGAN)
flower border
