| 摘要: |
| 【目的】人工智能算法能否有效习得风景园林设计特征是一个值得探讨的问题。【方法】采用样式生成对抗网络2代（style generative adversarial network2, StyleGAN2）算法，通过算法训练生成风景园林设计方案；之后拆解StyleGAN2算法中的w向量，采用主成分分析（principal component analysis, PCA）降维方法和无监督学习K均值聚类方法可视化w向量特征；最后根据w向量的数据特征和生成结果的关联，分析算法对设计方案特征的提取能力。【结果】StyleGAN2可以为不同类型的场地生成高质量和多样化的设计方案，并且可以识别和提取一些高维抽象设计特征，如植被密度、水域面积、铺装面积、道路网络结构等。【结论】神经网络不仅可以识别图像形态特征，还可以在没有人类指导的情况下，无监督学习识别部分抽象的高维设计特征。但目前大部分设计特征耦合性较高，这是风景园林工作的复杂性和算法低可解释性共同导致的，需要未来进一步探索。 |
| 关键词: 风景园林 神经网络 深度学习 生成设计 生成对抗网络 可解释性 |
| DOI：10.12409/j.fjyl.202305050212 |
| 分类号:TU986；TP18 |
| 基金项目: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）；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）；北京高校高精尖学科建设项目“城乡人居生态环境学” |
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| Generation and Design Feature Recognition of Landscape Architecture Scheme Based on Style-Based Generative Adversarial Network |
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CHEN Ran, ZHAO Jing
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Beijing Forestry University
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| Abstract: |
| [Objective] This research explores a new question: how can artificial intelligence (AI) understand design features? This question is important and urgent for the field of landscape architecture, which can benefit from the new possibilities offered by AI technology. Especially, some image generation models based on deep learning, such as Midjourney, Dall-E, Stable Diffusion and other new tools, can create creative images based on simple user input, and seem to be able to produce satisfactory design results. However, can they capture the essence, logic and rules of design works? Or are they just generating graphics based on graphics? Despite their significant theoretical and practical implications, the aforesaid questions also face huge challenges and involve a number of problems. This research focuses on one aspect of these questions: How can AI algorithms identify and recognize high-dimensional design features based on StyleGAN (a style-based generative adversarial network)? This is a challenging technical problem that involves both design understanding and feature disentanglement. The research aims to use StyleGAN to train design schemes, capture the latent space features inside the StyleGAN algorithm, analyze whether the algorithm can recognize abstract design features of landscape architecture schemes, what features it can recognize, and whether it can disentangle feature coupling. [Methods] The research adopts StyleGAN as the main method to generate and analyze landscape architecture schemes. StyleGAN is a style-based generative adversarial network proposed by Karras et al. in 2018, which aims to generate high-quality, high-resolution and diverse images. It can control different levels of style features to achieve fine-grained editing of generated images. The StyleGAN algorithm consists of two parts: a mapping network and a synthesis network. The mapping network can transform a random noise vector z into a latent vector w, which contains different levels of style features. The synthesis network can generate an image from a constant vector by progressively adding details from coarse to fine resolution. The style features are injected into each layer of the synthesis network by adaptive instance normalization (AdaIN) operations. The research adopts two datasets for training: one is a general dataset with 4,047 diverse design schemes collected from public sources; the other one is a directional dataset with 105 “multiple solutions for one problem” schemes for a specific site in Beijing. The research trains two generators (a general generator and a directional generator) based on StyleGAN2 model with 512 × 512 resolution. The research adopts two techniques to analyze the latent vector w: dimensionality reduction and truncation trick. Dimensionality reduction is used to visualize and cluster w vectors in a two-dimensional space by principal component analysis (PCA) and k-means methods. Truncation trick is used to manipulate and edit w vectors by changing their influence strength on different layers of the synthesis network. The truncation trick is adopted to compare each generated scheme with an “average scheme” that erases specific design features, and thus infers what kind of design features are contained in each w vector. [Results] The research shows the analysis results in two parts: data feature analysis and semantic information analysis. In data feature analysis, the research adopts PCA to reduce the dimensionality of w vectors and compare them with z vectors, finding that w vectors have more distinctive features than z vectors, which are close to standard normal distribution. The research also adopts k-means to cluster w vectors and embed images into them finding that w vectors can roughly extract and classify some features from diverse design schemes, but the classification logic is different for different categories. Some categories are based on morphology, water area, hard-soft ratio, road network structure, park type and other design features, while some others are based on the frequency of appearance of certain design nodes. In semantic information analysis, the research adopts truncation trick to manipulate w vectors by changing their influence strength from 0 to 1, finding find that w vectors can control different levels of design features in generated schemes, such as vegetation density, water area, pavement area, road network structure and other high-level design attributes. The research also finds that some features are entangled with each other, which means that changing one feature may affect other features as well. This is due to the complexity of landscape design and the difficulty of feature disentanglement. [Conclusion] The research concludes that AI algorithms can identify and extract some high-dimensional design features from landscape architecture schemes, not only image morphology, but also semantic-rich design features. However, most features are still difficult to disentangle due to the complexity of landscape design and the uninterpretability of algorithms. The research proposes that it is necessary to conduct feature disentanglement before exploring how AI algorithms understand design logic and rules, and that feature interpretation is an important topic for intelligent evidence-based design research, as it can help constrain algorithms to meet designers’ needs. |
| Key words: landscape architecture neural network deep learning generative design generative adversarial network interpretability |
