Applicability Evaluation and Reflection on Artificial Intelligence-based "Image to Image" Generation of Landscape Architecture Masterplans

Abstract

Artificial intelligence (AI) image generation is revolutionizing traditional workflow in landscape architecture industry, among which the "image-to-image" generative adversarial network (GAN) exhibits potential to facilitate concept design. Therefore, it underscores the importance of applicability evaluation from the perspective of users. This research 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. 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 findings indicate that the GAN-generated layout exhibits a high similarity to the human-designed layout, and the GAN-rendered masterplans fulfill the criteria for concept design and garner positive user acceptance. Conclusively, this study delves into the intrinsic rationality of the GAN generation methods and limitations in professional ethics and data bias, reflecting on the gaps between current AI-assisted design methods and evidence-based design.

● Quantitative applicability evaluation of "image to image" landscape masterplan generation method

● Image analysis reveals a high similarity between GAN-generated and human-designed layouts

● User survey reveals a high visual realism and practitioners' high acceptance of GAN-rendered masterplans

● Identifies the intrinsic rationality of current GAN generation methods and the technical gaps between these methods and evidence-based design

Graphical abstract

Keywords

Landscape Architecture / Image Generation / Generative Adversarial Network / Artificial Intelligence-Assisted Design / Applicability Evaluation / Landscape Masterplan

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