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February 5, 2026

Deep learning-based segmentation of rock discontinuities from photogrammetry and LiDAR: A geotechnical case study from Armenia

Puntos clave

The research aims to improve the identification and segmentation of rock discontinuities using advanced deep learning techniques.
Utilized photogrammetry and LiDAR to gather high-resolution 3D datasets.
Applied deep learning models, specifically Convolutional Neural Networks (CNNs).
Implemented image segmentation architectures like U-Net for classification tasks.
Evaluated models on their ability to classify surface features accurately.
Successfully segmented rock discontinuities with high accuracy.
Demonstrated the effectiveness of CNNs and U-Net in distinguishing planar and non-planar surfaces.
Results suggest a significant reduction in subjectivity compared to traditional methods.

Resumen

Accurate identification and segmentation of rock discontinuities are essential for geotechnical and geological research, especially for slope stability analysis and construction planning. Traditional field methods are time-consuming and prone to subjectivity. Remote sensing technologies such as LiDAR and photogrammetry produce high-resolution datasets that, when combined with deep learning methods, enable efficient and objective rock surface analysis. This study presents a comprehensive approach to detecting and classifying rock discontinuities using deep learning models applied to 3D data derived from LiDAR and photogrammetry. We evaluate the performance of Convolutional Neural Networks (CNNs) and image segmentation architectures like U-Net in classifying surface features and distinguishing planar and non-planar discontinuities.

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