scholarly journals Increasing Spatio-Temporal Resolution for Monitoring Alpine Solifluction Using Terrestrial Laser Scanners and 3D Vector Fields

2021 ◽  
Vol 13 (6) ◽  
pp. 1192
Author(s):  
Christoph Holst ◽  
Jannik Janßen ◽  
Berit Schmitz ◽  
Martin Blome ◽  
Malte Dercks ◽  
...  
Keyword(s):  
Temporal Resolution ◽  
Point Cloud ◽  
Vector Fields ◽  
Feature Detection ◽  
Water Saturation ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
The Arctic ◽  
High Mountain ◽  
Mountain Areas

This article investigates the usage of terrestrial laser scanner (TLS) point clouds for monitoring the gradual movements of soil masses due to freeze–thaw activity and water saturation, commonly referred to as solifluction. Solifluction is a geomorphic process which is characteristic for hillslopes in (high-)mountain areas, primarily alpine periglacial areas and the arctic. The movement can reach millimetre-to-centimetre per year velocities, remaining well below the typical displacement mangitudes of other frequently monitored natural objects, such as landslides and glaciers. Hence, a better understanding of solifluction processes requires increased spatial and temporal resolution with relatively high measurement accuracy. To that end, we developed a workflow for TLS point cloud processing, providing a 3D vector field that can capture soil mass displacement due to solifluction with high fidelity. This is based on the common image-processing techniques of feature detection and tracking. The developed workflow is tested on a study area placed in Hohe Tauern range of the Austrian Alps with a prominent assemblage of solifluction lobes. The derived displacements were compared with the established geomonitoring approach with total station and signalized markers and point cloud deformation monitoring approaches. The comparison indicated that the achieved results were in the same accuracy range as the established methods, with an advantage of notably higher spatial resolution. This improvement allowed for new insights considering the solifluction processes.

scholarly journals An Elevation Ambiguity Resolution Method Based on Segmentation and Reorganization of TomoSAR Point Cloud in 3D Mountain Reconstruction

2021 ◽  
Vol 13 (24) ◽  
pp. 5118
Author(s):  
Xiaowan Li ◽  
Fubo Zhang ◽  
Yanlei Li ◽  
Qichang Guo ◽  
Yangliang Wan ◽  
...  
Keyword(s):  
Ambiguity Resolution ◽  
Point Cloud ◽  
Spatial Clustering ◽  
Point Clouds ◽  
Gaussian Mixture ◽  
High Mountain ◽  
Real Point ◽  
Resolution Method ◽  
Mountain Areas ◽  
External Data

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method.

scholarly journals Full 4D Change Analysis of Topographic Point Cloud Time Series using Kalman Filtering

2022 ◽  
Author(s):  
Lukas Winiwarter ◽  
Katharina Anders ◽  
Daniel Schröder ◽  
Bernhard Höfle
Keyword(s):  
Time Series ◽  
Kalman Filter ◽  
Spatial Data ◽  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
High Mountain ◽  
Change Analysis ◽  
Area Of Interest ◽  
Rainy Weather

Abstract. 4D topographic point cloud data contain information on surface change processes and their spatial and temporal characteristics, such as the duration, location, and extent of mass movements, e.g., rockfalls or debris flows. To automatically extract and analyse change and activity patterns from this data, methods considering the spatial and temporal properties are required. The commonly used M3C2 point cloud distance reduces uncertainty through spatial averaging for bitemporal analysis. To extend this concept into the full 4D domain, we use a Kalman filter for point cloud change analysis. The filter incorporates M3C2 distances together with uncertainties obtained through error propagation as Bayesian priors in a dynamic model. The Kalman filter yields a smoothed estimate of the change time series for each spatial location, again associated with an uncertainty. Through the temporal smoothing, the Kalman filter uncertainty is, in general, lower than the individual bitemporal uncertainties, which therefore allows detection of more change as significant. In our example time series of bi-hourly terrestrial laser scanning point clouds of around 6 days (71 epochs) showcasing a rockfall-affected high-mountain slope in Tyrol, Austria, we are able to almost double the number of points where change is deemed significant (from 14.9 % to 28.6 % of the area of interest). Since the Kalman filter allows interpolation and, under certain constraints, also extrapolation of the time series, the estimated change values can be temporally resampled. This can be critical for subsequent analyses that are unable to deal with missing data, as may be caused by, e.g., foggy or rainy weather conditions. We demonstrate two different clustering approaches, transforming the 4D data into 2D map visualisations that can be easily interpreted by analysts. By comparison to two state-of-the-art 4D point cloud change methods, we highlight the main advantage of our method to be the extraction of a smoothed best estimate time series for change at each location. A main disadvantage of not being able to detect spatially overlapping change objects in a single pass remains. In conclusion, the consideration of combined temporal and spatial data enables a notable reduction in the associated uncertainty of the quantified change value for each point in space and time, in turn allowing the extraction of more information from the 4D point cloud dataset.

scholarly journals An Automatic 3D Point Cloud Registration Method Based on Biological Vision

2021 ◽  
Vol 11 (10) ◽  
pp. 4538
Author(s):  
Jinbo Liu ◽  
Pengyu Guo ◽  
Xiaoliang Sun
Keyword(s):  
Point Cloud ◽  
Feature Detection ◽  
Surface Deformation ◽  
Point Clouds ◽  
Transformation Matrix ◽  
Point Cloud Registration ◽  
Biological Vision ◽  
Registration Method ◽  
Shape Fitting ◽  
Initial Values

When measuring surface deformation, because the overlap of point clouds before and after deformation is small and the accuracy of the initial value of point cloud registration cannot be guaranteed, traditional point cloud registration methods cannot be applied. In order to solve this problem, a complete solution is proposed, first, by fixing at least three cones to the target. Then, through cone vertices, initial values of the transformation matrix can be calculated. On the basis of this, the point cloud registration can be performed accurately through the iterative closest point (ICP) algorithm using the neighboring point clouds of cone vertices. To improve the automation of this solution, an accurate and automatic point cloud registration method based on biological vision is proposed. First, the three-dimensional (3D) coordinates of cone vertices are obtained through multi-view observation, feature detection, data fusion, and shape fitting. In shape fitting, a closed-form solution of cone vertices is derived on the basis of the quadratic form. Second, a random strategy is designed to calculate the initial values of the transformation matrix between two point clouds. Then, combined with ICP, point cloud registration is realized automatically and precisely. The simulation results showed that, when the intensity of Gaussian noise ranged from 0 to 1 mr (where mr denotes the average mesh resolution of the models), the rotation and translation errors of point cloud registration were less than 0.1° and 1 mr, respectively. Lastly, a camera-projector system to dynamically measure the surface deformation during ablation tests in an arc-heated wind tunnel was developed, and the experimental results showed that the measuring precision for surface deformation exceeded 0.05 mm when surface deformation was smaller than 4 mm.

scholarly journals Automatic Processing Method for Deformation Monitoring of Circle Tunnels Based on 3D LiDAR Data

2018 ◽  
Author(s):  
Xiongyao Xie ◽  
Mingrui Zhao ◽  
Jiamin He ◽  
Biao Zhou
Keyword(s):  
Spatial Data ◽  
Point Cloud ◽  
Clustering Algorithm ◽  
Single Point ◽  
Point Clouds ◽  
Automatic Processing ◽  
Deformation Monitoring ◽  
Processing Method ◽  
Health State ◽  
3D Lidar

The application of 3D LiDAR technology has become increasingly extensive in tunnel monitoring due to the large density and high accuracy of the acquired spatial data. The proposed processing method aims at circle tunnels and provides a clear workflow to automatically process raw point data and easily interpretable results to analyze tunnel health state. The proposed automatic processing method employs a series of algorithms to extract point cloud of a single tunnel segment without obvious noise from entire raw tunnel point cloud mainly by three steps: axis acquisition, segments extraction and denoising. Tunnel axis is extracted by fitting boundaries of the tunnel point cloud rejection in plane with RANSAC algorithm. With guidance of axis, the entire preprocessed tunnel point cloud is segmented by equal division to get a section of tunnel point cloud which corresponds to a single tunnel segment. Then the noise in every single point cloud segment is removed by clustering algorithm twice, based on distance and intensity. Finally, clean point clouds of tunnel segments are processed by effective deformation extraction processor to get ovality and three-dimensional deformation nephogram.

scholarly journals Identification of stable areas in unreferenced laser scans for automated geomorphometric monitoring

2018 ◽  
Vol 6 (2) ◽  
pp. 303-317 ◽  
Author(s):  
Daniel Wujanz ◽  
Michael Avian ◽  
Daniel Krueger ◽  
Frank Neitzel
Keyword(s):  
Point Cloud ◽  
Region Of Interest ◽  
Point Clouds ◽  
Deformation Monitoring ◽  
Cloud Data ◽  
Laser Scanners ◽  
Research Questions ◽  
Iterative Closest Point Algorithm ◽  
The Impact

Abstract. Current research questions in the field of geomorphology focus on the impact of climate change on several processes subsequently causing natural hazards. Geodetic deformation measurements are a suitable tool to document such geomorphic mechanisms, e.g. by capturing a region of interest with terrestrial laser scanners which results in a so-called 3-D point cloud. The main problem in deformation monitoring is the transformation of 3-D point clouds captured at different points in time (epochs) into a stable reference coordinate system. In this contribution, a surface-based registration methodology is applied, termed the iterative closest proximity algorithm (ICProx), that solely uses point cloud data as input, similar to the iterative closest point algorithm (ICP). The aim of this study is to automatically classify deformations that occurred at a rock glacier and an ice glacier, as well as in a rockfall area. For every case study, two epochs were processed, while the datasets notably differ in terms of geometric characteristics, distribution and magnitude of deformation. In summary, the ICProx algorithm's classification accuracy is 70 % on average in comparison to reference data.

scholarly journals Dense 3D displacement vector fields for point cloud-based landslide monitoring

Landslides ◽  
2021 ◽  
Author(s):  
Zan Gojcic ◽  
Lorenz Schmid ◽  
Andreas Wieser
Keyword(s):  
Point Cloud ◽  
Vector Fields ◽  
Displacement Vector ◽  
Point Clouds ◽  
Deformation Analysis ◽  
Landslide Monitoring ◽  
Point Cloud Data ◽  
Traditional Methods ◽  
Cloud Data ◽  
Displacement Vectors

AbstractWe propose a novel fully automated deformation analysis pipeline capable of estimating real 3D displacement vectors from point cloud data. Different from the traditional methods that establish displacements based on the proximity in the Euclidean space, our approach estimates dense 3D displacement vector fields by searching for corresponding points across the epochs in the space of 3D local feature descriptors. Due to this formulation, our method is also sensitive to motion and deformations that occur parallel to the underlying surface. By enabling efficient parallel processing, the proposed method can be applied to point clouds of arbitrary size. We compare our approach to the traditional methods on point cloud data of two landslides and show that while the traditional methods often underestimate the displacements, our method correctly estimates full 3D displacement vectors.

scholarly journals A Comparison Method for 3D Laser Point Clouds in Displacement Change Detection for Arch Dams

2021 ◽  
Vol 10 (3) ◽  
pp. 184
Author(s):  
Yijing Li ◽  
Ping Liu ◽  
Huokun Li ◽  
Faming Huang
Keyword(s):  
Change Detection ◽  
Point Cloud ◽  
Point Clouds ◽  
Comparison Method ◽  
Deformation Monitoring ◽  
Arch Dam ◽  
Arch Dams ◽  
3D Point Clouds ◽  
Contour Model ◽  
States Data

Dam deformation monitoring can directly identify the safe operation state of a dam in advance, which plays an important role in dam safety management. Three-dimensional (3D) terrestrial laser scanning technology is widely used in the field of deformation monitoring due to its fast, complete, and high-density 3D data acquisition capabilities. However, 3D point clouds are characterized by rough surfaces, discrete distributions, which affect the accuracy of deformation analysis of two states data. In addition, it is impossible to directly extract the correspondence points from an irregularly distributed point cloud to unify the coordinates of the two states’ data, and the correspondence lines and planes are often difficult to obtain in the natural environment. To solve the above problems, this paper studies a displacement change detection method for arch dams based on two-step point cloud registration and contour model comparison method. In the environment around a dam, the stable rock is used as the correspondence element to improve the registration accuracy, and a two-step registration method from rough to fine using the iterative closest point algorithm is present to describe the coordinate unification of the two states’ data without control network and target. Then, to analyze the displacement variation of an arch dam surface in two states and improve the accuracy of comparing the two surfaces without being affected by the roughness of the point cloud, the contour model fitting the point clouds is used to compare the change in distance between models. Finally, the method of this paper is applied to the Xiahuikeng Arch Dam, and the displacement changes of the entire dam in different periods are visualized by comparing with the existing methods. The results show that the displacement change in the middle area of the dam is generally greater than that of the two banks, increasing with the increase in elevation, which is consistent with the displacement change behavior of the arch dam during operation and can reach millimeter-level accuracy.

Can point cloud data be used to calculate time to failure of a landslide?

2020 ◽  
Author(s):  
Andy Take ◽  
Nancy Berg ◽  
Toshikazu Hori
Keyword(s):  
Temporal Resolution ◽  
Point Cloud ◽  
Shear Failure ◽  
Point Clouds ◽  
Image Correlation ◽  
Earth Dam ◽  
High Temporal Resolution ◽  
Time To Failure ◽  
Point Cloud Data ◽  
Cloud Data

<p>Point cloud data capturing ground surface elevation at two instants in time are commonly used to identify the occurrence of landslides, identify their spatial extent, and to provide an estimate of the volume of depletion/accretion. In this study, it is hypothesized that this same point cloud data has the potential to yield much more valuable quantitative information regarding landslide behaviour, including the direction, magnitude, and rate of surface displacement.  Given point cloud data contains roughness information, shaded projections (hillshade images) of the slope at two or more instants in time can be processed using digital image correlation (DIC) to track displacement in the plane of the projection. If multiple view angles are used to generate the hill shade images, 3D surface displacements of the landslide surface should theoretically be resolved. Furthermore, if point clouds are generated with sufficiently high temporal resolution, it should be possible to estimate the time to failure. We explore this hypothesis in field experiment conducted in Tsukuba, Japan in which we bring a 3.5 m high earth dam to shear failure under high reservoir levels and extreme rainfall. Point clouds of the downstream dam surface generated at high temporal resolution were successfully used to calculate the 3D displacement of the dam surface, and to calculate the time of failure using the inverse-velocity method to within four minutes of the observed slope failure.</p>

scholarly journals HIERARCHICAL PROXIMITY-BASED OVER-SEGMENTATION OF 3-D POINT CLOUDS FOR EFFICIENT GRAPH FEATURE DETECTION

2020 ◽  
Vol V-2-2020 ◽  
pp. 273-280
Author(s):  
Z. Shtain ◽  
S. Filin
Keyword(s):  
Point Cloud ◽  
Surface Characterization ◽  
Feature Detection ◽  
Point Clouds ◽  
Free Form ◽  
Natural Scenes ◽  
General Strategy ◽  
Computationally Efficient ◽  
Definition Of ◽  
Point Cloud Simplification

Abstract. Point cloud simplification is empowered by the definition of similarity metrics which we aim to identify homogeneous regions within the point-cloud. Nonetheless, the variety of shapes and clutter in natural scenes, along with the significant resolution variations, occlusions, and noise, contribute to inconsistencies in the geometric properties, thereby making the homogeneity measurement challenging. Thus, the objective of this paper is to develop a point-cloud simplification model by means of data segmentation and to extract information in a better-suited way. The literature shows that most approaches either apply volumetric data strategies and/or resort to simplified planar geometries, which relate to only part of the entities found within a natural scene. To provide a more general strategy, we propose a proximity-based approach that allows an efficient and reliable surface characterization with no limitation on the number or shape of the primitives which in turn, enables detecting free-form objects. To achieve this, a local, computationally efficient and scalable metric is developed, which captures resolution variation and allows for short processing time. Our proposed scheme is demonstrated on datasets featuring a variety of surface types and characteristics. Experiments show high precision rates while exhibiting robustness to the varying resolution, texture, and occlusions that exist within the sets.

scholarly journals Automatic Recognition of Pole-Like Objects from Mobile Laser Scanning Point Clouds

2018 ◽  
Vol 10 (12) ◽  
pp. 1891 ◽  
Author(s):  
Zhenwei Shi ◽  
Zhizhong Kang ◽  
Yi Lin ◽  
Yu Liu ◽  
Wei Chen
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Shape Matching ◽  
Feature Detection ◽  
Three Dimensional ◽  
Point Clouds ◽  
Information Support ◽  
Point Cloud Data ◽  
Cloud Data ◽  
Linear Feature

Mobile Laser Scanning (MLS) point cloud data contains rich three-dimensional (3D) information on road ancillary facilities such as street lamps, traffic signs and utility poles. Automatically recognizing such information from point cloud would provide benefits for road safety inspection, ancillary facilities management and so on, and can also provide basic information support for the construction of an information city. This paper presents a method for extracting and classifying pole-like objects (PLOs) from unstructured MLS point cloud data. Firstly, point cloud is preprocessed to remove outliers, downsample and filter ground points. Then, the PLOs are extracted from the point cloud by spatial independence analysis and cylindrical or linear feature detection. Finally, the PLOs are automatically classified by 3D shape matching. The method was tested based on two point clouds with different road environments. The completeness, correctness and overall accuracy were 92.7%, 97.4% and 92.3% respectively in Data I. For Data II, that provided by International Society for Photogrammetry and Remote Sensing Working Group (ISPRS WG) III/5 was also used to test the performance of the method, and the completeness, correctness and overall accuracy were 90.5%, 97.1% and 91.3%, respectively. Experimental results illustrate that the proposed method can effectively extract and classify PLOs accurately and effectively, which also shows great potential for further applications of MLS point cloud data.

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