Artificial Intelligence and Landscape Architecture: The Aesthetic and Sustainable Fabric of The Future
DOI:
https://doi.org/10.53463/ecopers.20240313Keywords:
Artificial intelligence, Data analysis, Environmental sensitivity, Landscape architecture, SustainabilityAbstract
Artificial intelligence (AI) is driving transformative changes in landscape architecture, reshaping the sector. This study examines AI's innovative role in landscape design and its future potential. AI algorithms accelerate design processes and enable data-driven decision-making through comprehensive analyses of large datasets. While increasing the efficiency of design processes, this technology also contributes significantly to sustainability and environmental sensitivity. By promoting the efficient use of natural resources, AI aids in reducing environmental impact and strengthens the sustainability of future landscape projects. AI-driven analyses provide designers with innovative and effective solutions, especially in areas such as water resource management, climate-adapted plant selection, and ecosystem preservation. However, awareness-building training programs are needed among designers to ensure AI’s effective use. The integration of AI into landscape architecture not only guides sector innovations but also contributes to the creation of new career opportunities. This study takes a comprehensive look at AI's role and future impact in landscape architecture.
References
Batty, M. (2018). Artificial intelligence and smart cities. Environment and Planning B: Urban Analytics and City Science, 45(1), 3-6. https://doi.org/10.1177/2399808317751169
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
Ergül, D. B., Malkoçoğlu, A. B. V., & Özgünler, S. A. (2022). Mimari tasarım karar verme süreçlerinde yapay zekâ tabanlı bulanık mantık sistemerinin değerlendirilmesi. Journal of Architectural Sciences and Applications, 7(2), 878-899. https://doi.org/10.30785/mbud.1117910
Fernberg, P., & Chamberlain, B. (2023). Artificial intelligence in landscape architecture: A literature review. Landscape Journal, 42(1), 13-35. https://doi.org/10.3368/lj.42.1.13
Gölgelioğlu, C. (2023). Günümüz yapay zekâ araçlarının kentsel tasarım alanındaki potansiyelleri üzerine: Midjourney ve ChatGPT örnekleri. 47. Dünya Şehircilik Günü Kolokyumu. https://www.researchgate.net/profile/Can-Goelgelioglu-2/publication/382659435_Gunumuz_yapay_zeka_araclarinin_kentsel_tasarim_alanindaki_potansiyelleri_uzerine_Midjourney_ve_ChatGPT_ornekleri/links/66a8c8b6c6e41359a84a3c65/Guenuemuez-yapay-zeka-araclarinin-kentsel-tasarim-alanindaki-potansiyelleri-uezerine-Midjourney-ve-ChatGPT-oernekleri.pdf
Gül, L. F., Delikanlı, B., Üneşi, O., & Gül, E. (2024). Yapay Zeka, yaratıcılığı destekleyen bir takım arkadaşı olabilir mi? Mimari tasarım stüdyosu deneyiminden öğrendiklerimiz. 1-12. https://www.researchgate.net/publication/386215284_Yapay_Zeka_Yaraticiligi_Destekleyen_bir_Takim_Arkadasi_Olabilir_mi_Mimari_Tasarim_Studyosu_Deneyiminden_Ogrendiklerimiz
Güney, C. (2016). Yeni nesil coğrafi bilgi sistemlerinde yapay zeka. XVIII. Akademik Bilişim Konferansı (AB 2016). https://ab.org.tr/ab16/bildiri/224.pdf
Han, J., Kamber, M., & Pei, J. (2012). Data Mining: Concepts and Techniques (3. bs.). https://doi.org/10.1016/C2009-0-61819-5
Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics). https://www.sas.upenn.edu/~fdiebold/NoHesitations/BookAdvanced.pdf
İşler, B., & Kılıç, M. (2021). Eğitimde yapay zekâ kullanımı ve gelişimi. Yeni Medya Elektronik Dergisi, 5(1), 1-11.
Koutra, S., & Ioakimidis, C. S. (2022). Unveiling the potential of machine learning applications in urban planning challenges. Land, 12(1). https://doi.org/10.3390/land12010083
Sanchez, T. W., Fu, X., Yigitcanlar, T., & Ye, X. (2024). The research landscape of AI in urban planning: A topic analysis of the literature with ChatGPT. Urban Science, 8(4). https://doi.org/10.3390/urbansci8040197
Tang, X., & Chung, W. (2024). Automated urban landscape design: An AI-driven model for emotion-based layout generation and appraisal. PeerJ Computer Science, 10, 1-16. https://doi.org/10.7717/peerj-cs.2426
Tazefidan, C., Eşme, E., & Başar, M. E. (2022). Mimarlık Alanında Yapay Zeka Uygulamalarının Kullanımına Yönelik Bir Literatür Araştırması. Art & Design: II. International Congress on Art and Design Research – Book of Proceedings, 986-1002. https://hdl.handle.net/20.500.13091/5844
Zhang, J., Zhan, Q., & Liang, H. (2024). Application research of artificial intelligence algorithms in energy-efficient design for low-carbon building landscapes. International Journal of Low-Carbon Technologies, 19, 2814-2821. https://doi.org/10.1093/ijlct/ctae244
Zhu N., Liu X., Liu Z., Hu K., Wang Y., Tan J., Huang M., Zhu Q., Ji X., Jiang Y., & Guo Y. (2018). Deep learning for smart agriculture: Concepts, tools, applications, and opportunities. International Journal of Agricultural and Biological Engineering, 11(4), 32-44. https://doi.org/10.25165/j.ijabe.20181104.4475
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