Peyzaj mimarlığında yapay zeka uygulamaları: Yapay zeka üretimi peyzaj tasarımlarının ekolojik performansının Sites kriterleri üzerinden değerlendirilmesi
Applications of artificial intelligence in landscape architecture: Evaluation of the ecological performance of ai-generated landscape designs based on Sites criteria
Yazar: MEHMET ONUR SENEM
Danışman: PROF. DR. HAYRİYE EŞBAH TUNÇAY
İstanbul Teknik Üniversitesi / Bilişim Enstitüsü / Bilişim Uygulamaları Ana Bilim Dalı / Coğrafi Bilgi Teknolojileri Bilim Dalı
Derece: Doktora, 2024
Konu: Peyzaj Mimarlığı = Landscape Architecture

Özet:
Bu çalışmada Türkiye'de 2000-2023 yılları arası açılan Kentsel Tasarım Projesi Yarışmalarında kazanan projeler üretken yapay zeka araçlarının derin öğrenme sürecinde veri seti olarak olarak kullanılmıştır. Bu veri seti ile eğitilen üretken yapay zeka araçlarından alınan çıktılar Dünya'nın genelinde kabul gören derecelendirme sistemlerinden Sustainable SITEs'ın mekansal ve zamansal ölçeklere bağımlı kriterlerine göre ölçülmeyi hedeflemektedir.
Tez araştırması; "İnsanlar tarafından tasarlanan vaziyet planları ile YZ tarafından tasarlanan vaziyet planlarını ekolojik kriterlere göre sistematik olarak nasıl karşılaştırabiliriz?" sorusuna cevap vermeyi ve yapay zekanın ekolojik verimliliğinin anlaşılmasını amaçlamaktadır. Çalışma kapsamında tanımlanan veri seti depolama, yazılım ve donanım araçları gibi tüm araştırma bileşenleri bulut ortamında yönetilmiştir. Açık kaynaklardan elde edilen kazanan projelerin vaziyet planları 35 tanesinden veri seti oluşturulmuş ve Google Drive adlı bulut ortamında depolanmıştır. Araştırmanın donanım ihtiyacını Google Colaboratory (Colab) T4 GPU (Graphical Processing Unit) karşılamaktadır. Colab içerisinde python 3 dilinde yazılan çalışmanın veri seti sayısı ile verimli çalışma yeteneğine sahip üretken yapay sinir ağlarından FastGAN ile derin öğrenme süreci gerçekleştirilmiştir. Arazi kullanım örüntüsü, oran ve ilişkilerini anlaması için eğitilen FastGAN modeli bulut ortamında saklanmış ve istenilen zamanda ve sayıda çıktı vermektedir. Üretken yapay sinir ağından alınan çıktılar Diffusion Modellerden, Stable Diffusion v1.6 kullanılarak öğrenilen örüntü, oran ve ilişkiler yüksek çözünürlüklü kolay okunan peyzaj planları haline getirilmiştir.
Dünya'da önde gelen proje derecelendirme sistemlerinden olan Sustainable SITEs'ın tasarım, tasarım ve tasarım geliştirme aşamaları ile ilgili kriterleri arazi kullanım bilgileri üzerinden ölçülmektedir. Bu sayede veri seti olan projeler ve aynı sayıda projeye sahip üretken yapay zeka çıktıları Sustainable SITEs'ın ekolojik kriterleri üzerinden ölçülmüştür. Bu yöntem ile insan yapımı planlar ve yapay zeka üretimi planlar ekolojik kriterler üzerinden karşılaştırılmıştır.
Çalışmanın bulguları insan üretimi planların, yapay zeka üretimi planlardan ekolojik olarak daha toplam puan olarak yüksek puan aldığıdır. Bazı kriter puanlarının eşit çıkması ilgi çekicidir. Yapay zeka üretimi planlarının insan üretimlerini geçtiği kriterler de bulunmaktadır.
Yapay sinir ağının derin öğrenme aşamasında veya Diffusion Model aşamasında yeni veri setleri ve yazılım geliştirmeler ile yapay zeka üretiminin insan karşısında ekolojik olarak yüksek performanslı plan üretimi yapabileceği çalışma kapsamında anlaşılmıştır. Bu çalışma kapsamında peyzaj tasarımında yapay zeka'nın ekolojik katkısının anlaşılması ve potansiyellerinin keşfedilmesi gelecekte peyzaj mimarlığı alanında yapılacak çalışmalarda kritik rol oynaması beklenmektedir.

Abstract:
In this research, relevant studies have been analysed from the discovery of AI to the present day, when it is discussed as an effective tool for design disciplines interested in the built environment. The thesis investigated the trends in artificial intelligence and deep learning in disciplines dealing with the built environment. 
In this context, it examined the researches on the built environment using Generative Adversarial Networks algorithms. It has been tried to understand and evaluate all deep learning related researches carried out by design disciplines within the scope of landscape architecture, to explore their potentials and to understand the applications to be developed in the future.
From the production of floor plans to the urban scale, all artificial intelligence studies on the built environment and their contribution to the discipline of landscape architecture have been investigated. Within the scope of this thesis, previous studies in different disciplines were also examined for generative models that can be developed in the field of landscape architecture related to the use of artificial intelligence. The development of a system that will produce artificial intelligence generated landscape plans is an important goal of this thesis research. What kind of data set to work with for the developed system is a very important detail. Answers to very important questions such as the content, scope and quality of the data were sought. This issue is very important due to the important role that the data set will play in artificial intelligence and deep learning studies in landscape architecture. Different methods, such as using the dataset with a single generative-AI tool and using it with different generative tools and with each other, are experiments in the research process. As a result of these researches, the method in which the research will use generative AI, deep learning tools has been defined.
One of the most important issues addressed in the thesis is to understand how generative-AI tools can be measured ecologically and how they will contribute to ecological sustainability. Understanding the ecological sustainability of the plans produced by AI within the scope of the thesis and investigating the ecological measurement of generative-AI productions required a comprehensive effort. It is understood from the literature review that developing a system that can be measured ecologically has the potential to serve as a starting point for future generative AI studies. 
Therefore, widely accepted project scoring and evaluation systems have been studied, and the measurement of generative AI production plans has been investigated in a systematic and organized manner. It has been understood that a systematic and organized measurement system dependent on scale is necessary for the development of ecologically measurable systems.
Within the scope of this study, the issues where AI and deep learning intersect with ethics are also examined. With the evaluations made on the review researches, it was aimed to identify the aspects related to the thesis and ethics and to understand the components of artificial intelligence and ethics. Risks in ethics and artificial intelligence, future dangers and threats, and risks in authorship are discussed. The dark side issues of artificial intelligence, speculative areas are not examined in depth because they are not the subject of the thesis, but the issues related to the thesis are examined.
In this study, winning projects from Urban Design Project Competitions held in Turkey between 2000 and 2023 have been utilized as a dataset in the deep learning process of generative artificial intelligence tools. The outputs obtained from these AI tools trained with this dataset aim to be measured according to the spatial and temporal criteria of the globally accepted rating system Sustainable SITES.
The thesis aims to answer the question, "How can we systematically compare site plans designed by people with those designed by AI based on ecological criteria?" and intends to comprehend the ecological efficiency of artificial intelligence. All research components, such as the defined dataset storage, software, and hardware tools, have been managed in a cloud environment. Site plans of the winning projects obtained from open sources have constituted a dataset of 35 projects and stored in a cloud environment named Google Drive. The hardware requirement of the research is met by Colaboratory (Colab) T4 GPU (Graphical Processing Unit).
The deep learning process with efficient productivity capability has been carried out using FastGAN, a generative neural network written in Python 3 language, based on the number of datasets within Colab. The FastGAN model trained to understand land use patterns, ratios, and relationships is stored in the cloud and provides outputs at any desired time and quantity. The outputs from the generative neural network have been transformed into high-resolution, easily readable landscape plans using Diffusion Models, utilizing Stable Diffusion v1.6. 
The criteria related to the design, design, and design development stages of Sustainable SITES, one of the leading project rating systems globally, are measured based on land use pattern, ratios and relationship information. Thus, projects with datasets and an equal number of outputs from generative artificial intelligence have been measured against the ecological criteria of Sustainable SITES. In this way, human-made plans and artificial intelligence-generated plans have been compared based on ecological criteria. 
The findings of the study indicate that human-generated plans receive higher overall scores compared to AI-generated plans ecologically. It is noteworthy that some criterion scores are equal. There are also criteria where AI-generated plans surpass human productions.
It is understood within the scope of the study that with new datasets and software developments in the deep learning stage of artificial neural networks or the Diffusion Model stage, artificial intelligence production can achieve high-performance plan production ecologically compared to humans. It is expected that understanding the ecological contribution of artificial intelligence in landscape design and exploring its potentials within this study will play a critical role in future studies in landscape architecture.