Araştırma Makalesi

Text to Image in Landscape Architecture: Artificial Intelligence Approaches

Cilt: 18 Sayı: 4 15 Temmuz 2025
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Text to Image in Landscape Architecture: Artificial Intelligence Approaches

Öz

Generating ideas for designing spaces with creative and diverse concepts in landscape architecture design is a time-consuming process. With technological advancements, effective use of time has become increasingly significant, and applications that produce creative, realistic, and elaborate visuals from text in the field of artificial intelligence (AI) have attracted attention. However, the use of AI applications in landscape architecture raises many questions regarding the rapid and accurate development of design ideas through the Text-to-Image (T2I) method: how satisfying are AI-generated visuals in terms of professional accuracy and aesthetics? Can AI accurately perceive professional terms? What is the general relationship between AI and landscape architecture? Apart from coming up with an answer to these questions, this study aims to investigate the potential of text-to-image (T2I) models in generating design concepts for landscape architecture. Fifty professional terms were selected and used to create 70-word prompts across six distinct design concepts. Four popular T2I models (Dall-E, MidJourney, LookX, and mnml) were employed to generate visuals based on these prompts. The images generated were evaluated based on their adherence to professional standards, aesthetic appeal, creativity, and technical accuracy. Results indicated that while AI models could effectively interpret a wide range of professional terms, including abstract and highly technical concepts, there were limitations in capturing the nuanced details of landscape design. This study highlights the potential of AI to assist landscape architects in the early stages of the design process, but also underscores the need for human expertise to refine and optimize AI-generated designs. Future research should explore ways to improve the accuracy and specificity of AI-generated landscape designs, as well as investigate the potential of integrating AI with other design tools and techniques.

Anahtar Kelimeler

Kaynakça

  1. Agnese, J., Herrera, J., Tao, H. & Zhu, X. (2019). A survey and Taxonomy of Adversarial Neural Networks for Text-to-image Synthesis. WIREs Data Mining and Knowledge Discovery. https://doi.org/10.1002/widm.1345.
  2. Anonymous (2023, August). Mimarların ve Tasarımcıların Bilmesi Gereken 10 Yapay Zeka Uygulaması. XXI. https://xxi.com.tr/i/mimarlarin-ve-tasarimcilarin-bilmesi-gereken-10-yapay-zeka-uygulamasi.
  3. Ardhianto, P., Santosa, Y. P., Moniaga, C., Utami, M. P., Dewi, C., Christanto, H. J., Chen, A. P. S. (2023). Generative Deep Learning for Visual Animation in Landscape Design. Hindawi Scientific Programming. https://doi.org/10.1155/2023/9443704
  4. Aslan, T. & Aydın, K. (2023). Metinden Görüntü Üretme Potansiyeli Olan Yapay Zekâ Sistemleri Sanat ve Tasarım Performanslarının İncelenmesi. Ondokuz Mayıs University Journal of Faculty of Education, 42(2), 1149-1198. https://doi.org/10.7822/omuefd.1293657.
  5. Benliay, A. & Kılıç, A. (2024). Peyzaj Tasarımı Sunum Tekniklerinde Yapay Zeka Uygulamalarının Değerlendirilmesi. PEYZAJ – Eğitim, Bilim, Kültür ve Sanat Dergisi. 6(1), 1-14. DOI: 10.53784/peyzaj.1490265
  6. Bengesi, S., El-Sayed, H., Sarker, M., Houkpati, Y., Irungu, J., & Oladunni, T. (2023). Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers. IEEE Access, 12, 69812-69837.
  7. Chen, B., Zhang, Z., Langrene, N., & Zhu, S. (2023). Unleashing the Potential of Prompt Engineering in Large Language Models: A Comprehensive Review. ArXiv.
  8. Dang, H., Mecke, L., Lehmann, F., Goller, S., & Buschek, D. (2022). How to Prompt? Opportunities and Challenges of Zero- and Few-Shot Learning for Human-AI Interaction in Creative Applications of Generative Models. ArXiv.

Ayrıntılar

Birincil Dil

İngilizce

Konular

Peyzaj Tasarımı

Bölüm

Araştırma Makalesi

Yayımlanma Tarihi

15 Temmuz 2025

Gönderilme Tarihi

20 Kasım 2024

Kabul Tarihi

25 Mart 2025

Yayımlandığı Sayı

Yıl 2025 Cilt: 18 Sayı: 4

Kaynak Göster

APA
Kahvecioğlu, C., Ast, M. C., & Sağlık, A. (2025). Text to Image in Landscape Architecture: Artificial Intelligence Approaches. Kent Akademisi, 18(4), 1824-1844. https://doi.org/10.35674/kent.1588484
AMA
1.Kahvecioğlu C, Ast MC, Sağlık A. Text to Image in Landscape Architecture: Artificial Intelligence Approaches. Kent Akademisi. 2025;18(4):1824-1844. doi:10.35674/kent.1588484
Chicago
Kahvecioğlu, Ceren, Mahmut Can Ast, ve Alper Sağlık. 2025. “Text to Image in Landscape Architecture: Artificial Intelligence Approaches”. Kent Akademisi 18 (4): 1824-44. https://doi.org/10.35674/kent.1588484.
EndNote
Kahvecioğlu C, Ast MC, Sağlık A (01 Temmuz 2025) Text to Image in Landscape Architecture: Artificial Intelligence Approaches. Kent Akademisi 18 4 1824–1844.
IEEE
[1]C. Kahvecioğlu, M. C. Ast, ve A. Sağlık, “Text to Image in Landscape Architecture: Artificial Intelligence Approaches”, Kent Akademisi, c. 18, sy 4, ss. 1824–1844, Tem. 2025, doi: 10.35674/kent.1588484.
ISNAD
Kahvecioğlu, Ceren - Ast, Mahmut Can - Sağlık, Alper. “Text to Image in Landscape Architecture: Artificial Intelligence Approaches”. Kent Akademisi 18/4 (01 Temmuz 2025): 1824-1844. https://doi.org/10.35674/kent.1588484.
JAMA
1.Kahvecioğlu C, Ast MC, Sağlık A. Text to Image in Landscape Architecture: Artificial Intelligence Approaches. Kent Akademisi. 2025;18:1824–1844.
MLA
Kahvecioğlu, Ceren, vd. “Text to Image in Landscape Architecture: Artificial Intelligence Approaches”. Kent Akademisi, c. 18, sy 4, Temmuz 2025, ss. 1824-4, doi:10.35674/kent.1588484.
Vancouver
1.Ceren Kahvecioğlu, Mahmut Can Ast, Alper Sağlık. Text to Image in Landscape Architecture: Artificial Intelligence Approaches. Kent Akademisi. 01 Temmuz 2025;18(4):1824-4. doi:10.35674/kent.1588484

Cited By

Kent Akademisi | Kent Kültürü ve Yönetimi Dergisi / Urban Academy | Journal of Urban Culture and Management

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