Generating conceptual landscape design via text-to-image generative AI model
Bibliographic Data
| ID | 21246882 |
|---|---|
| Authors | Xinyue Ye (0000-0001-8838-9476, Texas A&M University, corresponding author), Tianchen Huang (0000-0002-2664-4125, Texas A&M University), Yang Song (0000-0003-2734-9838, Texas A&M University), Xin Li (0000-0002-3425-8566, Texas A&M University), G Newman (0000-0003-4277-5931, Texas A&M University), Dayong Wu (0000-0003-0525-6374, Texas Department of Transportation), Dayong Jason Wu (Texas A&M Transportation Institute), Yijun Zeng (0009-0004-3288-9024, Iowa State University) |
| Year | 2025 |
| Volume | 52 |
| Issue | 8 |
| Pages | 1903-1919 |
| Publication date | 2025-10-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/23998083251316064 |
| PMID | 40949112 |
| OpenAlex | W4406955405 |
| Language | EN |
| References cited | 56 |
This study explores the integration of text-to-image generative AI, particularly Stable Diffusion, in conjunction with ControlNet and LoRA models in conceptual landscape design. Traditional methods in landscape design are often time-consuming and limited by the designer’s individual creativity, also often lacking efficiency in the exploration of diverse design solutions. By leveraging AI tools, we demonstrate a workflow that efficiently generates detailed and visually coherent landscape designs, including natural parks, city plazas, and courtyard gardens. Through both qualitative and quantitative evaluations, our results indicate that fine-tuned models produce superior designs compared to non-fine-tuned models, maintaining spatial consistency, control over scale, and relevant landscape elements. This research advances the efficiency of conceptual design processes and underscores the potential of AI in enhancing creativity and innovation in landscape architecture
Conceptual model · Generative Design · Generative grammar · Generative model · Natural language processing · 3D Surveying and Cultural Heritage · Computer Graphics and Visualization Techniques · Computer Science · Engineering · Remote Sensing and LiDAR Applications · Artificial Intelligence
| Highly cited | No |
|---|