Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification

被引:7
|
作者
Farmakis, Ioannis [1 ]
Bonneau, David [1 ]
Hutchinson, D. Jean [1 ]
Vlachopoulos, Nicholas [2 ]
机构
[1] Queens Univ, Dept Geol Sci & Geol Engn, Kingston, ON K7L 3N6, Canada
[2] Royal Mil Coll Canada, Dept Civil Engn, Kingston, ON K7K 7B4, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
3D point cloud; voxels; supervoxels; rock slope management; classification; knowledge extraction; semantics; object-oriented; change detection;
D O I
10.3390/rs13071354
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页数:22
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