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Article

Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification

1
Department of Geological Sciences and Geological Engineering, Queen’s University, Kingston, ON K7L 3N6, Canada
2
Department of Civil Engineering, Royal Military College of Canada, Kingston, ON K7K 7B4, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Daniele Giordan
Remote Sens. 2021, 13(7), 1354; https://doi.org/10.3390/rs13071354
Received: 22 January 2021 / Revised: 24 March 2021 / Accepted: 25 March 2021 / Published: 1 April 2021
(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)
Reality capture technologies, also known as close-range sensing, have been increasingly popular within the field of engineering geology and particularly rock slope management. Such technologies provide accurate and high-resolution n-dimensional spatial representations of our physical world, known as 3D point clouds, that are mainly used for visualization and monitoring purposes. To extract knowledge from point clouds and inform decision-making within rock slope management systems, semantic injection through automated processes is necessary. In this paper, we propose a model that utilizes a segmentation procedure which delivers segments ready to classify and be retained or rejected according to complementary knowledge-based filter criteria. First, we provide relevant voxel-based features based on the local dimensionality, orientation, and topology and partition them in an assembly of homogenous segments. Subsequently, we build a decision tree that utilizes geometrical, topological, and contextual information and enables the classification of a multi-hazard railway rock slope section in British Columbia, Canada into classes involved in landslide risk management. Finally, the approach is compared to machine learning integrating recent featuring strategies for rock slope classification with limited training data (which is usually the case). This alternative to machine learning semantic segmentation approaches reduces substantially the model size and complexity and provides an adaptable framework for tailored decision-making systems leveraging rock slope semantics. View Full-Text
Keywords: 3D point cloud; voxels; supervoxels; rock slope management; classification; knowledge extraction; semantics; object-oriented; change detection 3D point cloud; voxels; supervoxels; rock slope management; classification; knowledge extraction; semantics; object-oriented; change detection
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MDPI and ACS Style

Farmakis, I.; Bonneau, D.; Hutchinson, D.J.; Vlachopoulos, N. Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification. Remote Sens. 2021, 13, 1354. https://doi.org/10.3390/rs13071354

AMA Style

Farmakis I, Bonneau D, Hutchinson DJ, Vlachopoulos N. Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification. Remote Sensing. 2021; 13(7):1354. https://doi.org/10.3390/rs13071354

Chicago/Turabian Style

Farmakis, Ioannis, David Bonneau, D. J. Hutchinson, and Nicholas Vlachopoulos. 2021. "Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification" Remote Sensing 13, no. 7: 1354. https://doi.org/10.3390/rs13071354

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