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Journals / Coğrafya Dergisi / 2026 / Sayı: 36

Geomorphometry-Automatic Landform Classification

Jeomorfometri-Yeryüzü Şekillerinin Otomatik Belirlenmesi

Fatmagül Kılıç Gül(YILDIZ TEKNİK ÜNİVERSİTESİ)0000-0003-3467-9288
Pages
15–26
Publication Date
2018-06-22
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

This paper emphasizes the importance of adopting machine learning to implement new models applicable to all terrains.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In the past, landforms were represented in physiographic and morphometric maps by hand drawing.
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Key Findings & Data:Presently, calculation of parameters, implementation of relationships with landforms, scaling, classification methods, topological relations, homogeneity, and generalizations during the transformation of 3D components of landforms, such as mountains, peaks, slopes, valleys, and plain, into 2D geometric elements in computers are being investigated.
💡
Conclusion & Impact:This paper emphasizes the importance of adopting machine learning to implement new models applicable to all terrains.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:SYM · Yeryüzü şekilleri · Sınıflandırma

Abstract

In the past, landforms were represented in physiographic and morphometric maps by hand drawing. With developments in digital elevation models (DEM), geographic information systems (GIS) and image analyses, automatic extraction of landforms from morphological parameters and data storage in databases is now possible and are actively utilized in various fields, such as geomorphology, soil science, and ecology. In the above scopes, DEM data forms the database of morphometric parameters, such as, relief, slope, curvatures, and topographic openness. Presently, calculation of parameters, implementation of relationships with landforms, scaling, classification methods, topological relations, homogeneity, and generalizations during the transformation of 3D components of landforms, such as mountains, peaks, slopes, valleys, and plain, into 2D geometric elements in computers are being investigated. In this study, the methods and applications for the automatic extraction of the landforms developed by researchers across different disciplines were reviewed. Classification methods were grouped as combined parameters method and unsupervised/supervised classification methods based on pixel/object. This paper emphasizes the importance of adopting machine learning to implement new models applicable to all terrains.

Özet

Yeryüzü şekilleri, geçmişte fizyografik ve morfometrik haritalarda elle çizilerek gösterilirken, jeomorfometri, sayısal yükseklik modelleri (SYM), görüntü işleme ve coğrafi bilgi sistemleri (CBS) alanındaki gelişmeler şekillerin otomatik çıkarılmasını, veri tabanlarında depolanmasını ve jeomorfoloji, toprak bilimi, ekoloji vb. pek çok alanda daha etkin kullanımını sağlamıştır. Bu tür çalışmalarda temel veri SYM ve ondan hesaplanan eğim, eğrisellik, yükseklik farkı, topografik açıklık vb. morfolojik parametrelerdir. Yeryüzünde bileşenleri üç boyutlu (3B) olan yamaç, düzlük, vadi vb. şekillerin sınırlarının, iki boyutlu (2B) geometrik elemanlara yazılımlar ile dönüştürülmesinde parametrelerin hesaplanması, şekillenme ile ilişkilerinin kurulması, ölçek, sınıflandırma yöntemi, yeryüzü şekillerinin doğada birbirlerine göre topolojik ilişkileri, homojenlik, genelleştirme halen araştırılan konular arasındadır. Bu çalışmada, farklı disiplinlere mensup araştırmacılar tarafından yeryüzü şekillerinin otomatik belirlenmesine yönelik geliştirilen yöntemler ve uygulamalar incelenmiş, yöntemler; parametrelerin kombinasyonu ile yapılan denetimsiz sınıflandırma; piksel tabanlı denetimsiz/denetimli sınıflandırma ve obje tabanlı sınıflandırma şeklinde ayrıştırılmıştır. Örüntüler, öğrenme tabanlı modeller vb. algoritmalar ile her tür araziye uygulanabilecek modellerin geliştirilmesinin önemi vurgulanmıştır.

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Cite This Article

Gül, F. K. (2018). Geomorphometry-Automatic Landform Classification. Coğrafya Dergisi, Sayı: 36, 15–26. https://doi.org/10.26650/JGEOG409177

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