In recent years, the rapid development and enhancement of image generation technologies and mapping tools driven by generative artificial intelligence (AI) have significantly impacted the traditional landscape design industry. Thus, it is pressing for landscape architects to delineate the relationship between image generation and landscape design and explore potential opportunities of practice and research. Research on masterplan generation primarily focuses on "image-to-image" generative adversarial network (GAN). The application of these tools has developed from the generation of architectural floor plans to generating building arrangements and massing relationships. Therefore, this study aims to evaluate the quality of the GAN-generated results, their effectiveness in integrating with design workflows, and the landscape architects' acceptance of the results through image analysis and user survey.
This study, which was published on the journal of Landscape Architecture Frontiers and entitled "Applicability Evaluation and Reflection on Artificial Intelligence-based "Image to Image" Generation of Landscape Architecture Masterplans", focuses on the adaptability evaluation of two key tasks in the Pix2Pix–BicycleGAN landscape masterplan generation workflow—layout generation and masterplan rendering.
The evaluation focuses on layout generation and masterplan rendering within the Pix2Pix–BicycleGAN workflow. The evaluation metrics of image analysis including block number absolute/Euclidean distance, histogram distance, and structural similarity index measure, were employed. Additionally, the online survey with two questionnaires was conducted to evaluate the visual realism and preference for color and texture of the GAN-generated results.
The image analysis results show that both the diversity similarity of land use distribution in GAN-generated layouts and human-designed layouts and the similarity between GAN-rendered masterplans and those rendered by designers achieved a high level. This research has several limitations. First, it did not include an ethical evaluation of GAN generation methods. . The questionnaires lacked focus on the originality of GAN generation methods, and future research needs to collect user opinions on ethical issues. Second, the established evaluation framework does not consider the diversity of GAN-generated layouts and data bias. Furthermore, while the Pix2Pix–BicycleGAN workflow evaluated in this study is representative, it does not reflect the latest technological iterations.
In addition, evidence-based design challenges GAN generation methods due to its relatively low interpretability. Therefore, connecting the morphological expressions generated by GAN models with the quantitative analyses (e.g., physical models) should be overcome for deeply integrating AI into design disciplines. As the diversity of GAN-generated layouts increases, future utilization of multi-optimization algorithms to screen and improve the layouts will help enhance the scientific decision of design. With the ongoing updates of generative algorithms, there are opportunities to gradually integrate physical models and optimization algorithms with AI models, significantly improving the interpretability and applicability of GAN generation methods.
