scholarly journals VO-SMOG: A VERSATILE, SMOOTH SEGMENT-BASED GROUND FILTER FOR POINT CLOUDS VIA MULTI-SCALE VOXELIZATION

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 59-66
Author(s):  
E. Che ◽  
A. Senogles ◽  
M. J. Olsen
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Mean Squared Error ◽  
Point Clouds ◽  
Process Data ◽  
Multi Scale ◽  
Digital Elevation ◽  
Real World Datasets ◽  
Aircraft System ◽  
Elevation Model

Abstract. Point clouds acquired by light detection and ranging (lidar) and photogrammetry technology (e.g., structure from motion/multi-view stereo-SfM/MVS) are widely used for various applications such topographic mapping due to their high resolution and accuracy. To generate a digital elevation model (DEM) or extract other features in the data, the ground points and non-ground points usually need to be separated first. This process, called ground filtering, can be tedious and time consuming as it requires substantial manual effort for high quality results. Although many have developed automated ground filtering algorithms, very few have the versatility to process data acquired from different scenes and systems. In this paper, we propose a versatile ground filter based on multi-scale voxelization and smooth segments, named Vo-SmoG. The proposed method introduces a novel voxelization approach, followed by isolated voxel filtering, lowest point filtering, local smooth filtering, and ground clustering. The result of the Vo-SmoG ground filtering is a classified point cloud. The effectiveness and efficiency of our method are demonstrated qualitatively and quantitatively. The quantitative evaluation consists of both point-wise and grid-wise comparisons. The recall, precision, and F1-score are over 97% in terms of classification while the root mean squared error (RMSE) of the DEM is within 0.1 m, which is on par with the reported vertical accuracy of the tested data. We further demonstrate the versatility of the Vo-SmoG via large-scale, real-world datasets collected from different environments with mobile laser scanning, airborne laser scanning, terrestrial laser scanning, uncrewed aircraft system (UAS)-SfM, and UAS-lidar.

scholarly journals Terrestrial Laser Scanning for the Detection of Coarse Grain Size Movement in a Mountain Riverbed

Water ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 2199 ◽  
Author(s):  
Walicka ◽  
Jóźków ◽  
Kasprzak ◽  
Borkowski
Keyword(s):  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Horizontal Displacement ◽  
Mountain Stream ◽  
Limited Information ◽  
Fluvial Transport ◽  
Digital Elevation ◽  
Load Transport ◽  
Elevation Model

Fluvial transport is a natural process that shapes riverbeds and the surrounding terrain surface, particularly in mountainous areas. Since the traditional techniques used for fluvial transport investigation provide only limited information about the bed load transport, recently, laser scanning technology has been increasingly incorporated into research to investigate this issue in depth. In this study, a terrestrial laser scanning technique was used to investigate the transport of individual boulders. The measurements were carried out annually from 2011 to 2016 on the Łomniczka River, which is a medium-sized mountain stream. The main goal of this research was to detect and determine displacements of the biggest particles in the mountain riverbed. The methodology was divided into two steps. First, the change zones were detected using two strategies. The first strategy was based on differential digital elevation model (DEM) creation and the second involved the calculation of differences between point clouds instead of DEMs. The experiments show that the second strategy was more efficient. In the second step, the displacements of the boulders were determined based on the detected areas of change. Using the proposed methodology, displacements for individual stones in each year were determined. Most of the changes took place in 2012–2014, which correlates well with the hydrological observations. During the six-year period, movements of individual particles with diameters less than 0.8 m were observed. Maximal displacements in the observed period reached 3 m. Therefore, it is possible to determine both vertical and horizontal displacement in the riverbed using multitemporal TLS.

scholarly journals Fusion of high resolution remote sensing images and terrestrial laser scanning for improved biomass estimation of maize

2014 ◽  
Vol XL-7 ◽  
pp. 101-108 ◽  
Author(s):  
C. Hütt ◽  
H. Schiedung ◽  
N. Tilly ◽  
G. Bareth
Keyword(s):  
High Resolution ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Satellite System ◽  
Biomass Estimation ◽  
Digital Elevation ◽  
Elevation Model ◽  
In The Beginning

In this study, images from the satellite system WorldView-2 in combination with terrestrial laser scanning (TLS) over a maize field in Germany are investigated. Simultaneously to the measurements a biomass field campaigns was carried out. From the point clouds of the terrestrial laser scanning campaigns crop surface models (CSM) from each scanning date were calculate to model plant growth over time. These results were resampled to match the spatial resolution of the WorldView-2 images, which had to orthorectified using a high resolution digital elevation model and atmosphere corrected using the ATCOR Software package. A high direct correlation of the NDVI calculated from the WorldView-2 sensor and the dry biomass was found in the beginning of June. At the same date, the heights from laser scanning can also explain a certain amount of the biomass variation (<i>r</i><sup>2</sup> = 0.6). By combining the NDVI from WorldView-2 and the height from the laser scanner with a linear model, the R2 reaches higher values of 0.86. To further understand the relationship between CSM derived crop heights and reflection indices, a comparison on a pixel basis was performed. Interestingly, the correlation of the NDVI and the crop height is rather low at the beginning of June (<i>r</i><sup>2</sup> = 0,4, <i>n</i> = 1857) and increases significantly (<i>R</i><sup>2</sup> = 0,79, <i>N</i> = 1857) at a later stage.

Development on Filtering Algorithms of Airborne LiDAR Point Clouds

2012 ◽  
Vol 226-228 ◽  
pp. 1892-1898
Author(s):  
Jian Qing Shi ◽  
Ting Chen Jiang ◽  
Ming Lian Jiao
Keyword(s):  
Remote Sensing ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Filtering Algorithms ◽  
Filtering Algorithm ◽  
Digital Elevation ◽  
City Model ◽  
Elevation Model

Airborne LiDAR is a new kind of surveying technology of remote sensing which developed rapidly during recent years. Raw laser scanning point clouds data include terrain points, building points, vegetation points, outlier points, etc.. In order to generate digital elevation model (DEM) and three-dimensional city model,these point clouds data must be filtered. Mathematical morphology based filtering algorithm, slope based filtering algorithm, TIN based filtering algorithm, moving surface based filtering algorithm, scanning lines based filtering algorithm and so on several representative filtering algorithms for LiDAR point clouds data have been introduced and discussed and contrasted in this paper. Based on these algorithms summarize the studying progresss about the filtering algorithm of airborne LiDAR point clouds data in home and abroad. In the end, the paper gives an expectation which will provides a reference for the following relative study.

scholarly journals A Workflow to Estimate Topographic and Volumetric Changes and Errors in Channel Sedimentation after Disturbance

2019 ◽  
Vol 11 (5) ◽  
pp. 586 ◽  
Author(s):  
Samira Nourbakhshbeidokhti ◽  
Alicia Kinoshita ◽  
Anne Chin ◽  
Joan Florsheim
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Multiple Model ◽  
Stream Channels ◽  
Change Analysis ◽  
Volumetric Change ◽  
Analytic Methods ◽  
Digital Elevation ◽  
Elevation Data ◽  
Elevation Model

Light Detection and Ranging (LiDAR) methods, such as ground-based Terrestrial Laser Scanning (TLS), have enabled collection of high-resolution point clouds of elevation data to calculate changes in fluvial systems after disturbance, but are often accompanied by uncertainty and errors. This paper reviews and compares TLS analysis methods and develops a workflow to estimate topographic and volumetric changes in channel sedimentation after disturbance. Four analytic methods to estimate topographic and volumetric changes were compared by quantifying the uncertainty in TLS-derived products: Digital Elevation Model (DEM) of difference (DOD), Cloud to Cloud (C2C), Cloud to Mesh (C2M), and Multiple Model to Model Cloud Comparison (M3C2). Mean errors across surfaces within each dataset contributed to a propagation error of 0.015–0.016 m and 0.017–0.018 m for the point clouds and derived DEMs, respectively. The estimated error of the total volumetric change implied increased errors in the conversion of point clouds into a surface by C2M and DOD; whereas C2C and M3C2 were generally simpler, efficient, and accurate techniques for evaluating topographic changes. The comparison of methods to analyze TLS data will contribute to applications of remote sensing of hydro-geomorphic processes in stream channels after disturbance. The workflow presented also aids in estimating uncertainties inherent in data collection and analytic methods for topographic and volumetric change analysis.

scholarly journals Large-scale map and 3D modelling of the Henryk Arctowski Polish Antarctic Station

Polar Record ◽  
2017 ◽  
Vol 53 (3) ◽  
pp. 280-288 ◽  
Author(s):  
Mariusz Pasik ◽  
Maria Elżbieta Kowalska ◽  
Sławomir Łapiński ◽  
Marcin Rajner ◽  
Krzysztof Bakuła
Keyword(s):  
Laser Scanning ◽  
Large Scale ◽  
Satellite System ◽  
Scientific Activity ◽  
3D Modelling ◽  
Future Research ◽  
Digital Elevation ◽  
The Mean ◽  
Elevation Model ◽  
Global Navigation Satellite

ABSTRACTThis paper presents survey measurements carried out during the 39th Polish Antarctic Expedition to the Henryk Arctowski Polish Antarctic Station in March 2015. The measurements were used to create a map on a 1:500 scale and for 3D modelling of the station buildings and vicinity. The paper also presents the geodetic control network established around the station. We discuss the issue of creating a digital elevation model for the station and its surroundings. The elevation models were generated using terrestrial laser scanning data integrated with Global Navigation Satellite System real time kinematic and tacheometric surveying. The accuracy of these models was estimated using height differences in relation to survey data. The mean height difference was 0.03 m and root mean square error was 0.05 m. Furthermore, an analysis of changes to the coastline was conducted using archival cartographic materials to assess the threat of Admiralty Bay to the station buildings. The results are important for continued scientific activity and safety at Arctowski Station, and may be useful for future research on King George Island.

scholarly journals Combination of UAV and terrestrial photogrammetry to assess rapid glacier evolution and map glacier hazards

2018 ◽  
Vol 18 (4) ◽  
pp. 1055-1071 ◽  
Author(s):  
Davide Fugazza ◽  
Marco Scaioni ◽  
Manuel Corti ◽  
Carlo D'Agata ◽  
Roberto Sergio Azzoni ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Data Sets ◽  
Survey Techniques ◽  
Digital Elevation ◽  
Standard Tool ◽  
Aerial Vehicle ◽  
Close Range ◽  
Elevation Model ◽  
Thinning Rate

Abstract. Tourists and hikers visiting glaciers all year round face hazards such as sudden terminus collapses, typical of such a dynamically evolving environment. In this study, we analyzed the potential of different survey techniques to analyze hazards of the Forni Glacier, an important geosite located in Stelvio Park (Italian Alps). We carried out surveys in the 2016 ablation season and compared point clouds generated from an unmanned aerial vehicle (UAV) survey, close-range photogrammetry and terrestrial laser scanning (TLS). To investigate the evolution of glacier hazards and evaluate the glacier thinning rate, we also used UAV data collected in 2014 and a digital elevation model (DEM) created from an aerial photogrammetric survey of 2007. We found that the integration between terrestrial and UAV photogrammetry is ideal for mapping hazards related to the glacier collapse, while TLS is affected by occlusions and is logistically complex in glacial terrain. Photogrammetric techniques can therefore replace TLS for glacier studies and UAV-based DEMs hold potential for becoming a standard tool in the investigation of glacier thickness changes. Based on our data sets, an increase in the size of collapses was found over the study period, and the glacier thinning rates went from 4.55 ± 0.24 m a−1 between 2007 and 2014 to 5.20 ± 1.11 m a−1 between 2014 and 2016.

scholarly journals A Strip Adjustment Method of UAV-Borne LiDAR Point Cloud Based on DEM Features for Mountainous Area

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2782
Author(s):  
Zequan Chen ◽  
Jianping Li ◽  
Bisheng Yang
Keyword(s):  
Laser Scanning ◽  
Ground Truth ◽  
Point Clouds ◽  
Mountainous Area ◽  
Mountainous Areas ◽  
Registration Method ◽  
Digital Elevation ◽  
Global Consistency ◽  
Manual Registration ◽  
Elevation Model

Due to the trajectory error of the low-precision position and orientation system (POS) used in unmanned aerial laser scanning (ULS), discrepancies usually exist between adjacent LiDAR (Light Detection and Ranging) strips. Strip adjustment is an effective way to eliminate these discrepancies. However, it is difficult to apply existing strip adjustment methods in mountainous areas with few artificial objects. Thus, digital elevation model-iterative closest point (DEM-ICP), a pair-wise registration method that takes topography features into account, is proposed in this paper. First, DEM-ICP filters the point clouds to remove the non-ground points. Second, the ground points are interpolated to generate continuous DEMs. Finally, a point-to-plane ICP algorithm is performed to register the adjacent DEMs with the overlapping area. A graph-based optimization is utilized following DEM-ICP to estimate the correction parameters and achieve global consistency between all strips. Experiments were carried out using eight strips collected by ULS in mountainous areas to evaluate the proposed method. The average root-mean-square error (RMSE) of all data was less than 0.4 m after the proposed strip adjustment, which was only 0.015 m higher than the result of manual registration (ground truth). In addition, the plane fitting accuracy of lateral point clouds was improved 4.2-fold, from 1.565 to 0.375 m, demonstrating the robustness and accuracy of the proposed method.

scholarly journals Segmentation and Multi-Scale Convolutional Neural Network-Based Classification of Airborne Laser Scanner Data

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3347 ◽  
Author(s):  
Zhishuang Yang ◽  
Bo Tan ◽  
Huikun Pei ◽  
Wanshou Jiang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Region Growing ◽  
Point Clouds ◽  
Classification Method ◽  
Multi Scale ◽  
Airborne Laser

The classification of point clouds is a basic task in airborne laser scanning (ALS) point cloud processing. It is quite a challenge when facing complex observed scenes and irregular point distributions. In order to reduce the computational burden of the point-based classification method and improve the classification accuracy, we present a segmentation and multi-scale convolutional neural network-based classification method. Firstly, a three-step region-growing segmentation method was proposed to reduce both under-segmentation and over-segmentation. Then, a feature image generation method was used to transform the 3D neighborhood features of a point into a 2D image. Finally, feature images were treated as the input of a multi-scale convolutional neural network for training and testing tasks. In order to obtain performance comparisons with existing approaches, we evaluated our framework using the International Society for Photogrammetry and Remote Sensing Working Groups II/4 (ISPRS WG II/4) 3D labeling benchmark tests. The experiment result, which achieved 84.9% overall accuracy and 69.2% of average F1 scores, has a satisfactory performance over all participating approaches analyzed.

scholarly journals Automated Visual Recognizability Evaluation of Traffic Sign Based on 3D LiDAR Point Clouds

2019 ◽  
Vol 11 (12) ◽  
pp. 1453 ◽  
Author(s):  
Shanxin Zhang ◽  
Cheng Wang ◽  
Lili Lin ◽  
Chenglu Wen ◽  
Chenhui Yang ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Laser Scanning ◽  
Large Scale ◽  
Evaluation Method ◽  
Evaluation Model ◽  
Point Clouds ◽  
Traffic Sign ◽  
Traffic Signs ◽  
3D Point Clouds ◽  
Traffic Environment

Maintaining the high visual recognizability of traffic signs for traffic safety is a key matter for road network management. Mobile Laser Scanning (MLS) systems provide efficient way of 3D measurement over large-scale traffic environment. This paper presents a quantitative visual recognizability evaluation method for traffic signs in large-scale traffic environment based on traffic recognition theory and MLS 3D point clouds. We first propose the Visibility Evaluation Model (VEM) to quantitatively describe the visibility of traffic sign from any given viewpoint, then we proposed the concept of visual recognizability field and Traffic Sign Visual Recognizability Evaluation Model (TSVREM) to measure the visual recognizability of a traffic sign. Finally, we present an automatic TSVREM calculation algorithm for MLS 3D point clouds. Experimental results on real MLS 3D point clouds show that the proposed method is feasible and efficient.

scholarly journals ANALYSIS OF 3D BUILDING MODELS ACCURACY BASED ON THE AIRBORNE LASER SCANNING POINT CLOUDS

2018 ◽  
Vol XLII-2 ◽  
pp. 797-804 ◽  
Author(s):  
W. Ostrowski ◽  
M. Pilarska ◽  
J. Charyton ◽  
K. Bakuła
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Large Scale ◽  
Point Clouds ◽  
3D Models ◽  
Airborne Laser Scanning ◽  
Aerial Images ◽  
Statistical Parameters ◽  
Airborne Laser ◽  
Building Models

Creating 3D building models in large scale is becoming more popular and finds many applications. Nowadays, a wide term “3D building models” can be applied to several types of products: well-known CityGML solid models (available on few Levels of Detail), which are mainly generated from Airborne Laser Scanning (ALS) data, as well as 3D mesh models that can be created from both nadir and oblique aerial images. City authorities and national mapping agencies are interested in obtaining the 3D building models. Apart from the completeness of the models, the accuracy aspect is also important. Final accuracy of a building model depends on various factors (accuracy of the source data, complexity of the roof shapes, etc.). In this paper the methodology of inspection of dataset containing 3D models is presented. The proposed approach check all building in dataset with comparison to ALS point clouds testing both: accuracy and level of details. Using analysis of statistical parameters for normal heights for reference point cloud and tested planes and segmentation of point cloud provides the tool that can indicate which building and which roof plane in do not fulfill requirement of model accuracy and detail correctness. Proposed method was tested on two datasets: solid and mesh model.

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