A Review of Generative Artificial Intelligence-Assisted Scenario Development Approaches in Landscape Architecture

Öz

In this article, we offer a comprehensive review of the evolution, progress and current state of Artificial Intelligence supported scenario generation research in Landscape Architecture. This technological development is enables a deep paradigm transformation centred on the shift from designing static “objects” to fostering dynamic “processes” and “systems” capable of self-adaption and evolve over time. The authors cover key generative methods such as Parametric Generatives Generative Design, focusing on rule-based optimization, Generative Adversarial Networks which leverage training via competition between networks and Text-to-Image models that generate instant conceptual visualisations. These are not merely tools to make artefacts of the design but sophisticated instruments for informed site analysis and performance-based optimization—the results of which involving hundreds if not thousands of design alternatives within minutes, are captured and tabulated efficiently. In addition, the study helps to understand crucial concerns regarding creativity, algorithmic bias and transparency. It emphasizes that Artificial Intelligence systems have the potential to exacerbate human biases present in training data and that these models can be so opaque as to create significant trust issues. In order to tackle these challenges, the paper calls for Explainable Artificial Intelligence techniques that provide interpretability, as well as User Interface/User Experience design tactics to minimize cognitive biases such as Confirmation Bias and Automation Bias. The dialogue progresses to discuss more sophisticated notions of Reinforcement Learning and World Models, in which Artificial Intelligence plays an active role in the design of dynamic, adaptive and regenerative landscape systems.

Anahtar Kelimeler

• G.A.R.D.E.N. framework
• generative artificial intelligence
• landscape architecture
• scenario generation

Peyzaj Mimarlığında Üretken Yapay Zeka Destekli Senaryo Geliştirme Yaklaşımlarına Bir Bakış

Öz

Bu makalede, Peyzaj Mimarlığı alanında Yapay Zeka destekli senaryo üretimi araştırmalarının evrimi, ilerlemesi ve mevcut durumu hakkında kapsamlı bir inceleme sunulmaktadır. Bu teknolojik gelişme aslında, statik “nesneler” tasarlamaktan, zaman içinde kendi kendine adapte olabilen ve gelişebilen dinamik “süreçler” ve “sistemler” geliştirmeye doğru kayan derin bir paradigma dönüşümünü mümkün kılmaktadır. Çalışmada, kural tabanlı optimizasyon olan Parametrik Üretken Tasarım, ağlar arasındaki rekabetçi eğitimden yararlanan Üretken Çekişmeli Ağlar ve anlık kavramsal görselleştirme üreten Metinden-Görüntüye modeller gibi temel üretken yöntemler ele alınmıştır. Bunlar, tasarımın çıktılarını ortaya koymak için bir araç olmanın yanı sıra, bilgilendirilmiş saha çalışması analizleri ve performansa dayalı optimizasyon için kullanılan araçlardır. Bu değerlendirmeler dakikalar içinde yüzlerce hatta binlerce tasarım alternatifi haline getirilmektedir. Bunun yanı sıra, çalışmada yaratıcılık, algoritmik önyargı ve şeffaflık ile ilgili kritik endişelerin anlaşılmasına eleştirel bir bakış açısıyla yaklaşılmıştır. Yapay zeka sistemlerinin, eğitim verilerinde mevcut olan insan önyargılarına dayalı sonuçlar üretme potansiyeline sahip olduğu ve bu nedenle bu modellerin, önemli güven sorunları yaratabilecekleri de vurgulanmaktadır. Böylesi olumsuz durumlara müdahale etmek için araştırmada, yorumlanabilirlik sağlayan Açıklanabilir Yapay Zeka tekniklerinin yanı sıra Onay Önyargısı ve Otomasyon Önyargısı gibi bilişsel önyargıları en aza indirmek için Kullanıcı Arayüzü/Kullanıcı Deneyimi tasarım sistemleri de ele alınmıştır. Araştırma, yapay zekanın statik çıktı üretmenin yanı sıra dinamik, uyarlanabilir ve rejeneratif peyzaj sistemlerinin tasarımında etken bir rol oynamayı içerdiği Pekiştirmeli Öğrenme ve Dünya Modelleri gibi daha sofistike kavramları tartışmaya açmıştır.

Anahtar Kelimeler

• G.A.R.D.E.N. tekniği
• üretken yapay zeka
• peyzaj mimarlığı
• senaryo üretimi

Kaynakça

1. Arup. (2024). AI for future cities: Urban planning and design. Arup. https://www.arup.com/insights/ai-for-future-cities-urban-planning-and-design/
2. Benliay, A., & Kiliç, A. (2024). Peyzaj tasarımı sunum tekniklerinde yapay zekâ uygulamalarının değerlendirilmesi. PEYZAJ - Eğitim, Bilim, Kültür ve Sanat Dergisi, 6(1), 1-14. https://doi.org/10.53784/peyzaj.1490265
3. Bhattacharjee, S. (2024). 5 Best Generative Design Software To Master in 2025. Novart. https://www.novatr.com/blog/generative-design-softwares
4. Chen R., Yi, X., Zhao J., He Y., Chen B., Liu F., Yao X., Jiang X., Lian Z. & Li H. (2025). AI for Landscape Planning: Assessing Surrounding Contextual Impact on GAN-Generated Green Land Layouts. Cities, 166, 106181. https://doi.org/10.1016/j.cities.2025.106181
5. Chen, R., Zhao, J., Yao, X., He, Y., Li, Y., Lian, Z., Han, Z., Yi, X., & Li, H. (2024). Enhancing Urban Landscape Design: A GAN-Based Approach for Rapid Color Rendering of Park Sketches. Land, 13(2), 254. https://doi.org/10.3390/land13020254
6. Chen, R., Zhao, J., Yao, X., Jiang, S., He, Y., Bao, B., Luo, X., Xu, S., & Wang, C. (2023). Generative Design of Outdoor Green Spaces Based on Generative Adversarial Networks. Buildings, 13(4), 1083. https://doi.org/10.3390/buildings13041083
7. Devi, A. (2025). Reinforcement Learning Applications in Autonomous Systems: From Traffic Optimization to Robotics. International Journal of Scientific Research & Engineering Trends, 11(2), 2388-2393. https://doi.org/10.61137/ijsret.vol.11.issue2.435
8. Dobre, J. (2025). Designing AI for human expertise: Preventing cognitive shortcuts. UX Matters.

9. Er, B.E., & Bozkurt, M. (2025). Artificial Intelligence and Learning from Nature in Landscape Architecture: An Innovative Approach that Shapes the Analysis & Design Process Journal of Digital Landscape Architecture, 10, 394-402.
10. Fernberg, P. J. (2024). Artificial intelligence in landscape architecture: A survey of theory, culture and practice [Unpublished Doctoral Dissertation]. Utah State University. https://digitalcommons.usu.edu/etd2023/152
11. Frazier, A. E. & Song, L. (2025). Artificial Intelligence in Landscape Ecology: Recent Advances, Perspectives and Opportunities. Curr Landscape Ecol Rep 10, 1. https://doi.org/10.1007/s40823-024-00103-7
12. Goyal, M., & Mahmoud, Q. H. (2024). A Systematic Review of Synthetic Data Generation Techniques Using Generative AI. Electronics, 13(17), 3509. https://doi.org/10.3390/electronics13173509
13. Ha, D., & Schmidhuber, J. (2018). World models. arXiv preprint arXiv:1803.10122. https://doi.org/10.5281/zenodo.1207631
14. Hakim, Y. F., & Tsai, F. (2025). Deep Learning-Based Land Cover Extraction from Very-High-Resolution Satellite Imagery for Assisting Large-Scale Topographic Map Production. Remote Sensing, 17(3), 473. https://doi.org/10.3390/rs17030473
15. Han, Y., Zhang, K., Xu, Y., Wang, H., & Chai, T. (2023). Application of Parametric Design in the Optimization of Traditional Landscape Architecture. Processes, 11(2), 639. https://doi.org/10.3390/pr11020639
16. Jindal J.A., Lungren M.P., & Shah N.H. (2024). Ensuring useful adoption of generative artificial intelligence in healthcare. J Am Med Inform Assoc, 31(6):1441-1444. https://doi.org/10.1093/jamia/ocae043
17. 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
18. Kim, J., Yang, H., & Min, K. (2024). DALS: Diffusion-Based Artistic Landscape Sketch. Mathematics, 12(2), 238. https://doi.org/10.3390/math12020238
19. Kim, Y., Lee, J., Kim, J., Ha, J., & Zhu, J.. (2023). Dense Text-to-Image Generation with Attention Modulation. arXiv preprint arXiv: 2308.12964. https://doi.org/10.48550/arXiv.2308.12964
20. Landscape Architecture Foundation (2018). Evaluating landscape performance: A guidebook for metrics and methods selection. Landscapeperformance.org. https://www.landscapeperformance.org/guide-to-evaluate-performance
21. Little, C., Elliot, M.J., Allmendinger, R. & Samani, S.S. (2021). Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study. arXiv preprint arXiv: 2112.01925. https://doi.org/10.48550/arXiv.2112.01925
22. Lu, H., Gaur, A. & Lacasse, M. (2024). Climate data for building simulations with urban heat island effects and nature-based solutions. Scientific Data, 11, 731. https://doi.org/ 10.1038/s41597-024-03532-5
23. Manzoni, J. L., de Paz, J. & Nistal, P. (2025). From parametric to generative design: The next evolution of Building Information Modelling (BIM). European Commission. https://build-up.ec.europa.eu/en/resources-and-tools/articles/parametric-generative-design-next-evolution-building-information
24. Netser, Y., Cochran Hameen, E. & Tang, P. (2025). Socio-Sustainable Architectural Design Through Ethical Implementation of Generative Design and Artificial Intelligence. Proceedings of the 23rd CIB World Building Congress, Volume 1, Article 402. https://docs.lib.purdue.edu/cib-conferences/vol1/iss1/402/
25. Paananen, V., Oppenlaender, J. & Visuri, A. (2023). Using text-to-image generation for architectural design ideation. International Journal of Architectural Computing, 22(3), 550-565. https://doi.org/10.1177/14780771231222783
26. Perera, P., Perera, S., Jin, X., Rashidi, M., Nanayakkara, S., Yazbek, G. & Yazbek, A. (2025) "Impact of Explainable Artificial Intelligence for Sustainable Built Environment," Proceedings of the 23rd CIB World Building Congress, Volume 1, Article 347. https://doi.org/10.7771/3067-4883.1668
27. Popescu, R., & Schut, A. (2023). Generative AI in creative design processes: a dive into possible cognitive biases. In De Sainz Molestina, D., Galluzzo, L., Rizzo, F., Spallazzo, D. (eds.), IASDR 2023: Life-Changing Design, 9-13 October, Milan, Italy. https://doi.org/10.21606/iasdr.2023.784
28. Qiu, F., Chen, X., & Ma, L. (2025). The G.A.R.D.E.N framework for parametric design: a literature review. Architectural Engineering and Design Management, 1–28. https://doi.org/10.1080/17452007.2025.2504030
29. Rath, S. (2023). ControlNet – Achieving Superior Image Generation Results. LearnOpenCV. https://learnopencv.com/controlnet/
30. Saabith, A. L. S., Vinothraj, T. & Fareez, M. M. M. (2024). Exploring the Landscape of Large Language Models: A Comprehensive Review of Current Technologies. International Journal of Research in Engineering and Science, 12(12), 166-188.
31. Santos, M. R. C. & Carvalho, L. C. (2025). AI-driven participatory environmental management: Innovations, applications and future prospects. Journal of Environmental Management, 373, 123864. https://doi.org/10.1016/j.jenvman.2024.123864
32. Schroth, O. & Maier, A. (2025). Integrating Generative Artificial Intelligence into the Landscape Architecture Design Process. Journal of Digital Landscape Architecture, 10, 665-675. https://doi.org/10.14627/537754063
33. Shao, Y., Ma, N., Chen, M., Zhang, C. & Cui, Y. (2025). Machine Learning in Landscape Architecture: A Comprehensive Review of Advancements, Applications and Future Directions. Buildings, 15(21), 3827. https://doi.org/10.3390/buildings1521382
34. Song, Z., Tian, W., Wei, H. & Chu, S. (2024). Multiple-objective control of stormwater basins using deep reinforcement learning. Journal of Hydroinformatics, 26(11): 2852–2866. https://doi.org/10.2166/hydro.2024.191
35. Spremulli, M. & Wright, R., (2023). LAN3016YF: Generative design in landscape architecture: Explorations and applications. Daniels Faculty of Architecture, Landscape and Design, University of Toronto. https://www.daniels.utoronto.ca/sites/default/files/2023-08/LAN3016YF%20LEC0102%20-%20Robert%20Wright%20%26%20Matthew%20Spremulli.pdf
36. Sutton, R. S. & Barto, A. G. (2018). Reinforcement Learning: An Introduction. The MIT Press.
37. Thamma, S.R. (2024). AI-Driven Spatial Design - Generative Applications in Architecture and Urban Planning. International Journal of Engineering Research & Computer Science and Engineering, 8(1), 186-193.
38. Wang, Y. & Lin, Y.S. (2023). Public participation in urban design with augmented reality technology based on indicator evaluation. Frontiers in Virtual Reality, 4, 1071355. https://doi.org/10.3389/frvir.2023.1071355
39. Wang, Y., Xie, L., & Huang, M. (2025). Landscape design concept generation combining cultural mapping technology and multimodal modeling. Scientific Reports. https://doi.org/10.1038/s41598-025-31088-w
40. Yun, J., & Kim, N. (2025). Environment-Based Prompt Framework for AI-Generated Façade Design. Journal of Integrated Design and Process Science. https://doi.org/10.1177/1092061725138961
41. Zhang, L., Rao, A. & Agrawala, M. (2023). Adding Conditional Control to Text-to-Image Diffusion Models. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 3813-3824). https://doiorg/10.1109/ICCV51070.2023.00355
42. Zhang, O. (2024). Exploring Color Styling in Landscape Design through Convolutional Neural Network-Based Image Style Transfer. In 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL) (pp. 89-92). https://doi.org/10.1109/CVIDL62147.2024.10604097