Jeodezi ve Jeoinformasyon Dergisi

2147-1339 2667-8519

TMMOB Harita ve Kadastro Mühendisleri Odası

10.9733/JGG.2025R0013.E

Photogrametry Photogrammetry and Remote Sensing

Fotogrametri Fotogrametri ve Uzaktan Algılama

Comparison of machine learning algorithm performances in digital terrain model generation

Sayısal arazi modeli oluşturmada makine öğrenme algoritma performanslarının karşılaştırılması

https://orcid.org/0009-0005-9285-1447

Özen

Abdullah Can

DOKUZ EYLÜL ÜNİVERSİTESİ, FEN BİLİMLERİ ENSTİTÜSÜ

https://orcid.org/0000-0003-0181-8376

Vupa Çilengiroğlu

Özgül

DOKUZ EYLÜL ÜNİVERSİTESİ, FEN FAKÜLTESİ

11 04 2025

12 2 179 193 06 04 2025 09 29 2025

2012

Journal of Geodesy and Geoinformation

LiDAR technology enables precise distance measurements by emitting laser pulses that reflect off surface objects, allowing for the calculation of spatial coordinates. Alongside spatial data associated color values of LiDAR points can be extracted from images captured by onboard cameras. As the laser beams reflect upon their initial contact with surfaces, the resulting point cloud must be appropriately classified to support specific analytical or operational objectives. This study uses different machine learning methods to sort and label LiDAR point cloud data into ground and non-ground points, then compares how well each method works. For this purpose, a dataset acquired by an unmanned aerial vehicle over the Democratic Republic of Congo was utilized. The dataset comprises 114,557 points, each described by three geometric features (DeltaH, Verticality, 3rd Eigenvalue) and two normalized color attributes (Red and Green Ratios), derived from RGB values. A total of ten machine learning algorithms were implemented and assessed. Among them, the XGBoost algorithm demonstrated the highest classification accuracy at 84.1%, while the Naive Bayes algorithm yielded the lowest accuracy, at 72.4%.

LiDAR teknolojisi, yüzey nesnelerinden yansıyan lazer darbeleri göndererek hassas mesafe ölçümleri yapılmasına olanak tanır ve bu sayede mekânsal koordinatların hesaplanması mümkün olur. Mekânsal verilerin yanı sıra, LiDAR noktalarına ait renk bilgileri de araç üzerindeki kameralarla çekilen görüntülerden elde edilebilir. Lazer ışınları yüzeylerle ilk temas ettikleri anda yansıdığından, ortaya çıkan nokta bulutunun belirli analizsel veya operasyonel amaçlara hizmet edebilmesi için uygun şekilde sınıflandırılması gerekmektedir. Bu çalışmada, LiDAR nokta bulutu verilerini sıralamak ve analiz etmek için çeşitli makine öğrenmesi yöntemleri kullanılmış ve her bir yöntemin performansı karşılaştırılmıştır. Bu amaçla, insansız hava aracı ile Demokratik Kongo Cumhuriyeti’nde elde edilen bir veri seti kullanılmıştır. Veri seti, üç geometrik özellik ve iki renk bilgisi içeren toplam 114 557 noktadan oluşmaktadır. On farklı makine öğrenmesi algoritması uygulanmış ve değerlendirilmiştir. Bu algoritmalar arasında XGBoost, %84.1 ile en yüksek sınıflandırma doğruluğunu gösterirken, Naive Bayes algoritması ile %72.4 ile en düşük doğruluğa ulaşılmıştır.

Remote sensing LiDAR Photogrammetry Machine learning Classification

Uzaktan algılama LiDAR Fotogrametri Makine öğrenmesi Sınıflandırma

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