An Effective Approach for Rock Mass Discontinuity Extraction Based on Terrestrial LiDAR Scanning 3D Point Clouds

Impacts of underground mining have been reduced by continuous environmental endeavors, scientific, and engineering research activities, whose main object is the behavior and control of the undermined rock mass and the subsequent surface subsidence. In the presented Velenje case of underground sublevel longwall mining where coal is being exploited both horizontal and vertical, backfilling processes and accompanying fracturing in the coal layer, and rock mass are causing uncontrolled subsidence of the surface above. 3D point clouds of the study were acquired in ten epochs and at excavation heights on the front were measured at the same epochs. By establishing a sectors layout in the observational area, smaller point clouds were obtained, to which planes were fitted and centroids of these planes then calculated. Centroid heights were analyzed with the FNSE model to estimate the time of consolidation and modified according to excavation parameters to determine total subsidence after a certain period. Proposed prognosis approaches for estimating consolidation of active subsidence and long term surface environmental protection measures have been proposed and presented. The C2C analysis of distances between acquired 3D point clouds was used for identification of surface subsidence, reclamation areas and sink holes, and for validation of feasibility and effectiveness of the proposed prognosis.

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Automatic characterization of rock mass discontinuities using 3D point clouds

Engineering Geology ◽

10.1016/j.enggeo.2019.05.008 ◽

2019 ◽

Vol 259 ◽

pp. 105131 ◽

Cited By ~ 6

Author(s):

Xiaojun Li ◽

Ziyang Chen ◽

Jianqin Chen ◽

Hehua Zhu

Keyword(s):

Rock Mass ◽

Point Clouds ◽

3D Point Clouds

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MULTI-SCALE BASED EXTRACION OF VEGETATION FROM TERRESTRIAL LiDAR DATA FOR ASSESSING LOCAL LANDSCAPE

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-3-w4-263-2015 ◽

2015 ◽

Vol II-3/W4 ◽

pp. 263-270 ◽

Cited By ~ 4

Author(s):

T. Wakita ◽

J. Susaki

Keyword(s):

Urban Areas ◽

Three Dimensional ◽

Point Clouds ◽

Lidar Data ◽

Data Sets ◽

Landscape Index ◽

Terrestrial Lidar ◽

Multi Scale ◽

3D Point Clouds ◽

Set Of Points

In this study, we propose a method to accurately extract vegetation from terrestrial three-dimensional (3D) point clouds for estimating landscape index in urban areas. Extraction of vegetation in urban areas is challenging because the light returned by vegetation does not show as clear patterns as man-made objects and because urban areas may have various objects to discriminate vegetation from. The proposed method takes a multi-scale voxel approach to effectively extract different types of vegetation in complex urban areas. With two different voxel sizes, a process is repeated that calculates the eigenvalues of the planar surface using a set of points, classifies voxels using the approximate curvature of the voxel of interest derived from the eigenvalues, and examines the connectivity of the valid voxels. We applied the proposed method to two data sets measured in a residential area in Kyoto, Japan. The validation results were acceptable, with F-measures of approximately 95% and 92%. It was also demonstrated that several types of vegetation were successfully extracted by the proposed method whereas the occluded vegetation were omitted. We conclude that the proposed method is suitable for extracting vegetation in urban areas from terrestrial light detection and ranging (LiDAR) data. In future, the proposed method will be applied to mobile LiDAR data and the performance of the method against lower density of point clouds will be examined.

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Using open-source software for extracting geomechanical parameters of a rock mass from 3D point clouds: Discontinuity set extractor and SMRTool

Rock Mechanics and Rock Engineering: From the Past to the Future ◽

10.1201/9781315388502-190 ◽

2016 ◽

Cited By ~ 3

Author(s):

A Riquelme ◽

R Tomás ◽

M Cano ◽

A Abellán

Keyword(s):

Rock Mass ◽

Open Source ◽

Open Source Software ◽

Point Clouds ◽

3D Point Clouds ◽

Geomechanical Parameters

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A new approach for semi-automatic rock mass joints recognition from 3D point clouds

Computers & Geosciences ◽

10.1016/j.cageo.2014.03.014 ◽

2014 ◽

Vol 68 ◽

pp. 38-52 ◽

Cited By ~ 102

Author(s):

Adrián J. Riquelme ◽

A. Abellán ◽

R. Tomás ◽

M. Jaboyedoff

Keyword(s):

Rock Mass ◽

Point Clouds ◽

New Approach ◽

3D Point Clouds

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A New Method for Automatic Extraction and Analysis of Discontinuities Based on TIN on Rock Mass Surfaces

Remote Sensing ◽

10.3390/rs13152894 ◽

2021 ◽

Vol 13 (15) ◽

pp. 2894

Author(s):

Xiang Wu ◽

Fengyan Wang ◽

Mingchang Wang ◽

Xuqing Zhang ◽

Qing Wang ◽

...

Keyword(s):

Rock Mass ◽

Clustering Algorithm ◽

Point Clouds ◽

New Method ◽

Rock Masses ◽

Average Deviation ◽

Density Peaks ◽

3D Point Clouds ◽

Discontinuity Sets ◽

Dip Angle

Light detection and ranging (LiDAR) can quickly and accurately obtain 3D point clouds on the surface of rock masses, and on the basis of this, discontinuity information can be extracted automatically. This paper proposes a new method to automatically extract discontinuity information from 3D point clouds on the surface of rock masses. This method first applies the improved K-means algorithm based on the clustering algorithm by fast search and find of density peaks (DPCA) and the silhouette coefficient in the cluster validity index to identify the discontinuity sets of rock masses, and then uses the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm to segment the discontinuity sets and to extract each discontinuity from a discontinuity set. Finally, the random sampling consistency (RANSAC) method is used to fit the discontinuities and to calculate their parameters. The 3D point clouds of the typical rock slope in the Rockbench repository is used to extract the discontinuity orientations using the new method, and these are compared with the results obtained from the classical approach and the previous automatic methods. The results show that, compared to the results obtained by Riquelme et al. in 2014, the average deviation of the dip direction and dip angle is reduced by 26% and 8%, respectively; compared to the results obtained by Chen et al. in 2016, the average deviation of the dip direction and dip angle is reduced by 39% and 40%, respectively. The method is also applied to an artificial quarry slope, and the average deviation of the dip direction and dip angle is 5.3° and 4.8°, respectively, as compared to the manual method. Furthermore, the related parameters are analyzed. The study shows that the new method is reliable, has a higher precision when identifying rock mass discontinuities, and can be applied to practical engineering.

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FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-iii-3-177-2016 ◽

2016 ◽

Vol III-3 ◽

pp. 177-184 ◽

Cited By ~ 79

Author(s):

Timo Hackel ◽

Jan D. Wegner ◽

Konrad Schindler

Keyword(s):

Computation Time ◽

Semantic Segmentation ◽

Point Clouds ◽

Computationally Efficient ◽

Terrestrial Lidar ◽

Semantic Classification ◽

Multi Scale ◽

3D Point Clouds ◽

Point Density ◽

Careful Handling

We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.

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Quantifying contemporary debris supply in a debris-covered glacier catchment using high-resolution repeat terrestrial LiDAR

10.5194/egusphere-egu2020-17710 ◽

2020 ◽

Author(s):

Rebecca Stewart ◽

Matthew Westoby ◽

Stuart Dunning ◽

Francesca Pellicciotti ◽

John Woodward

Keyword(s):

Spatial Distribution ◽

Large Scale ◽

Point Clouds ◽

Time Lapse ◽

Detection Algorithm ◽

Temporal Variations ◽

Connected Components ◽

Glacier Mass Balance ◽

Terrestrial Lidar ◽

3D Point Clouds

Glacial debris cover is increasing at a global scale in response to increasing temperatures and negative glacier mass balance. The last decade or so has seen an abundance of research which focuses on debris-covered glacier dynamics and supraglacial processes, such as ice-cliff back wasting and the development of supraglacial ponds. However, far fewer studies have focussed on improving understanding of debris supply to these systems over short- (months-years) or long (centennial-millennial) timescales. Existing work has attempted to quantify headwall erosion by calculating the ratio of supraglacial debris flux (the product of debris thickness and supraglacial velocity) to the headwall catchment area. Whilst these studies provide estimates of headwall erosion rates over long timescales, they are unable to capture subtle (or extreme) spatial and temporal variations in debris supply that operate over shorter timescales. Capturing this variation is important because it will allow predictions of the spatial distribution and volume of debris layers on debris-covered glaciers, which in turn will increase the accuracy of ablation modelling and future melt predictions for these systems. To quantify such variability, we conducted terrestrial LiDAR surveys of potential debris slopes at Miage Glacier, Italy, between July &#8211; September 2019. We acquired > 1.8 billion 3D points per catchment survey covering an approximate slope area of 7.7 km2, which supplies debris to ~33% of the glacierised area. Sequential 3D point clouds were co-registered using iterative closest point adjustment. Vegetated surfaces were automatically detected using the CloudCompare plugin CANUPO and removed from further analysis. The M3C2 change detection algorithm was used to calculate 3D change normal to the surface plane, and a 95th percentile confidence interval was applied to eliminate non-significant change. Connected components analysis was used to identify discrete rockfall events, estimate their dimensions, explore their magnitude-frequency and quantify their spatial distribution. We find at least one large failure which developed over a period of two weeks (validated by in situ time-lapse footage) and comprised an estimated volume of around 1 x 106 m3. This particular failure occurred from a recently (<10 years) deglaciated slope, lending support to the theory that large-scale slope response to glacial erosion can be rapid.

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FAST SEMANTIC SEGMENTATION OF 3D POINT CLOUDS WITH STRONGLY VARYING DENSITY

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iii-3-177-2016 ◽

2016 ◽

Vol III-3 ◽

pp. 177-184 ◽

Cited By ~ 3

Author(s):

Timo Hackel ◽

Jan D. Wegner ◽

Konrad Schindler

Keyword(s):

Computation Time ◽

Semantic Segmentation ◽

Point Clouds ◽

Computationally Efficient ◽

Terrestrial Lidar ◽

Semantic Classification ◽

Multi Scale ◽

3D Point Clouds ◽

Point Density ◽

Careful Handling

We describe an effective and efficient method for point-wise semantic classification of 3D point clouds. The method can handle unstructured and inhomogeneous point clouds such as those derived from static terrestrial LiDAR or photogammetric reconstruction; and it is computationally efficient, making it possible to process point clouds with many millions of points in a matter of minutes. The key issue, both to cope with strong variations in point density and to bring down computation time, turns out to be careful handling of neighborhood relations. By choosing appropriate definitions of a point’s (multi-scale) neighborhood, we obtain a feature set that is both expressive and fast to compute. We evaluate our classification method both on benchmark data from a mobile mapping platform and on a variety of large, terrestrial laser scans with greatly varying point density. The proposed feature set outperforms the state of the art with respect to per-point classification accuracy, while at the same time being much faster to compute.

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