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Dergiler / Anadolu Orman Araştırmaları Dergisi / 2026 / Sayı: 13

Peyzaj mimarlığında coğrafi bilgi sistemleri (CBS) tabanlı makine öğrenmesi araştırmaları: Bilimsel analiz ve eğilimler

Geographic information systems (GIS)-based machine learning research in landscape architecture: Scientific analysis and trends

Şeyma Yiğit Uzunali(Hatay Mustafa Kemal University, Department of Landscape Architecture, Hatay, Türkiye)0000-0003-0837-0703
Alper Uzunali(Hatay Mustafa Kemal University)0000-0002-3286-7317
Sayfa
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Yayın Tarihi
2026-08-24
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bulgular ML’nin çoğu çalışmada merkezi bir yöntemsel rol üstlendiğini ve ağırlıklı olarak tahmin ve modelleme amaçlı kullanıldığını göstermektedir.

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Amaç & Hipotez:Giriş ve Hedefler Bu çalışma, makine öğrenmesinin (ML) peyzaj mimarlığı alanındaki bilimsel üretimdeki yerini, özellikle Coğrafi Bilgi Sistemleri (CBS) ile entegrasyonu bağlamında incelemektedir.
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Temel Bulgular & Veri:İkinci aşamada ise CBS entegreli ML uygulamalarını içeren çalışmalar, önceden tanımlanmış kodlama kriterlerine dayalı içerik analizi ile sistematik olarak değerlendirilmiştir.
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Sonuç & Katkı:Bulgular ML’nin çoğu çalışmada merkezi bir yöntemsel rol üstlendiğini ve ağırlıklı olarak tahmin ve modelleme amaçlı kullanıldığını göstermektedir.
🔬Tür:Literatür & Derleme Çalışması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Bibliyometrik analiz · coğrafi bilgi sistemleri (CBS) · CBS-MÖ entegrasyonu

Özet

Giriş ve Hedefler Bu çalışma, makine öğrenmesinin (ML) peyzaj mimarlığı alanındaki bilimsel üretimdeki yerini, özellikle Coğrafi Bilgi Sistemleri (CBS) ile entegrasyonu bağlamında incelemektedir. Veri odaklı yaklaşımların artmasına rağmen, CBS–ML entegrasyonunun kapsamı ve niteliği yeterince ortaya konmamıştır. Bu doğrultuda çalışma, mevcut eğilimleri belirlemeyi, yöntemsel yaklaşımları ortaya koymayı ve bu kesişimdeki araştırma boşluklarını tespit etmeyi amaçlamaktadır. Yöntemler Araştırmada iki aşamalı bir analitik çerçeve benimsenmiştir. İlk aşamada, Web of Science (WoS) veri tabanında yer alan ML odaklı yayınlar üzerinden bibliyometrik analiz gerçekleştirilerek genel araştırma eğilimleri incelenmiştir. İkinci aşamada ise CBS entegreli ML uygulamalarını içeren çalışmalar, önceden tanımlanmış kodlama kriterlerine dayalı içerik analizi ile sistematik olarak değerlendirilmiştir. Bulgular ML’nin çoğu çalışmada merkezi bir yöntemsel rol üstlendiğini ve ağırlıklı olarak tahmin ve modelleme amaçlı kullanıldığını göstermektedir. Buna karşın CBS entegrasyonunun sınırlı olduğu ve daha çok planlama ile karar destek uygulamalarıyla ilişkilendirildiği belirlenmiştir. Tahmin ve mekânsal dağılım haritalarının baskınlığı, CBS–ML entegrasyonunun büyük ölçüde öngörü temelli yaklaşıldığını ortaya koymaktadır. Sonuçlar Özellikle peyzaj tasarımı alanında CBS ve ML’nin bütüncül entegrasyonunda önemli bir araştırma boşluğu olduğunu göstermektedir. Ayrıca, gelecekteki çalışmalar için kavramsal ve yöntemsel bir çerçeve sunmakta ve yapay zekâ temelli yaklaşımların CBS–ML entegrasyonunu dönüştürme potansiyeline dikkat çekmektedir.

Abstract

Background and Aims This study examines the role of machine learning (ML) in scientific production within landscape architecture, with a particular focus on its integration with Geographic Information Systems (GIS). Despite the growing adoption of data-driven approaches, the scope and nature of GIS–ML integration remain insufficiently explored. Accordingly, the study aims to identify current trends, reveal methodological patterns, and determine research gaps at this intersection. Methods A two-stage analytical framework was adopted. In the first stage, a bibliometric analysis was conducted on ML-related publications indexed in the Web of Science (WoS) database to examine general research trends. In the second stage, studies involving GIS-integrated ML applications were systematically evaluated through content analysis based on predefined coding criteria. Results The findings indicate that ML plays a central methodological role in most studies and is predominantly used for prediction and modeling purposes. In contrast, GIS integration remains limited and is mainly associated with planning and decision-support applications. The dominance of prediction and spatial distribution maps suggests that GIS–ML integration is largely approached from a predictive perspective. Conclusion The results highlight a significant research gap in the comprehensive integration of GIS and ML, particularly in landscape design. The study also provides a conceptual and methodological framework for future research and emphasizes the transformative potential of AI-driven approaches in reshaping GIS–ML integration.

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Bu Makaleye Atıf Yap

Uzunali, Ş. Y., Uzunali, A. (2026). Peyzaj mimarlığında coğrafi bilgi sistemleri (CBS) tabanlı makine öğrenmesi araştırmaları: Bilimsel analiz ve eğilimler. Anadolu Orman Araştırmaları Dergisi, Sayı: 13. https://doi.org/10.53516/ajfr.1936541

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