Lidar Nokta Bulutundan Ransac-Tabanlı Bina Çatı Düzlemi Çıkarımı İçin Bir Yaklaşım

Year 2021, Volume: 2 Issue: 2, 76 - 95, 29.09.2021

Murat Güler , Mustafa Türker

https://doi.org/10.48123/rsgis.970893

Abstract

Son yıllarda önemi hızla artan 3B şehir modellerinde bina modellemesi LiDAR verisinin en yaygın uygulama alanları arasında yer almaktadır. Bu çalışmada, hava LiDAR verisinden 3B RANSAC (RANdom SAmple Consensus) algoritması ile çatı düzlemlerinin çıkarımı ve geriçatımı için veri odaklı bir yaklaşım önerilmiş ve iki farklı alanda (A1 ve A2) test edilmiştir. İlk olarak yer filtreleme yapılmıştır. Sonra, sınıflandırma ile tespit edilen bina sınıfı nokta bulutundan her bir binaya ait nokta kümesini çıkarmak için bölge büyüme bölütleme algoritması uygulanmıştır. Çıkarılan çatı düzlemsel yüzeylerde bulunan gürültü, DBSCAN (Density Based Spatial Clustering of Applications with Noise) algoritması kullanılarak tespit edilmiş ve silinmiştir. Doğruluk analizleri için, duyarlık (precision-p), bütünlük (recall-r) ve F-skor (F-score) değerleri hesaplanmıştır. A1 çalışma alanı için ortalama p, r ve F-skor değerleri sırasıyla, %86, %87 ve %85 olarak bulunmuştur. A2 çalışma alanı için bu değerler sırasıyla, %92, %93 ve %92 olarak bulunmuştur. Nokta yoğunluğunun daha yüksek olması ve bina çatı geometrisinin daha düzgün olması, A2 çalışma alanı sonuçlarını olumlu yönde etkilemiştir. Ayrıca, A2 çalışma alanında gürültünün daha başarılı bir şekilde tespiti sağlanmış ve dolayısıyla bu da doğruluk oranlarını artırmıştır.

Keywords

3B şehir modeli, LiDAR, RANSAC, Yer filtreleme, Bölütleme, Geriçatma

An Approach for Ransac-Based Building Roof Plane Extraction from Lidar Point Cloud

Abstract

In 3D city modelling, the importance of which has increased rapidly in recent years, building modelling is among the most frequently used application areas of LiDAR data. In this study, a data-driven approach was proposed for the extraction and reconstruction of roof planes from aerial LiDAR data using 3D RANSAC (RANdom SAmple Consensus) algorithm and tested in two areas (A1 and A2). First, ground filtering was performed. Then, region growing segmentation algorithm was applied to extract point set of each building from the building class detected through classification. The noise that exists on the extracted planar surfaces were detected and removed using the DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm. For accuracy assessment, precision (p), recall (r), and F-score values were calculated. For study area A1, the mean values for p, r and F-score were computed as 86%, 87% and 85%, respectively. For study area A2, these values were computed as 92%, 93% and 92%, respectively. The higher density of point cloud and smoother roof geometry appear to have affected the results positively in study area A2. Besides, the noise was more successfully detected in study area A2, which increased the accuracy rates.

Keywords

3D city model, LiDAR, RANSAC, Ground filtering, Segmentation, Reconstruction

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