scholarly journals THE IQMULUS URBAN SHOWCASE: AUTOMATIC TREE CLASSIFICATION AND IDENTIFICATION IN HUGE MOBILE MAPPING POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 301-307
Author(s):  
J. Böhm ◽  
M. Bredif ◽  
T. Gierlinger ◽  
M. Krämer ◽  
R. Lindenberg ◽  
...  
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Point Clouds ◽  
Urban Trees ◽  
Automatic Identification ◽  
Mobile Mapping ◽  
3D Data ◽  
Hadoop Distributed File System ◽  
Individual Trees ◽  
Mapping Point

Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.

scholarly journals THE IQMULUS URBAN SHOWCASE: AUTOMATIC TREE CLASSIFICATION AND IDENTIFICATION IN HUGE MOBILE MAPPING POINT CLOUDS

2016 ◽  
Vol XLI-B3 ◽  
pp. 301-307 ◽  
Author(s):  
J. Böhm ◽  
M. Bredif ◽  
T. Gierlinger ◽  
M. Krämer ◽  
R. Lindenberg ◽  
...  
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Point Clouds ◽  
Urban Trees ◽  
Automatic Identification ◽  
Mobile Mapping ◽  
3D Data ◽  
Hadoop Distributed File System ◽  
Individual Trees ◽  
Mapping Point

Current 3D data capturing as implemented on for example airborne or mobile laser scanning systems is able to efficiently sample the surface of a city by billions of unselective points during one working day. What is still difficult is to extract and visualize meaningful information hidden in these point clouds with the same efficiency. This is where the FP7 IQmulus project enters the scene. IQmulus is an interactive facility for processing and visualizing big spatial data. In this study the potential of IQmulus is demonstrated on a laser mobile mapping point cloud of 1 billion points sampling ~ 10 km of street environment in Toulouse, France. After the data is uploaded to the IQmulus Hadoop Distributed File System, a workflow is defined by the user consisting of retiling the data followed by a PCA driven local dimensionality analysis, which runs efficiently on the IQmulus cloud facility using a Spark implementation. Points scattering in 3 directions are clustered in the tree class, and are separated next into individual trees. Five hours of processing at the 12 node computing cluster results in the automatic identification of 4000+ urban trees. Visualization of the results in the IQmulus fat client helps users to appreciate the results, and developers to identify remaining flaws in the processing workflow.

scholarly journals Deep Localization of Static Scans in Mobile Mapping Point Clouds

2021 ◽  
Vol 13 (2) ◽  
pp. 219
Author(s):  
Yufu Zang ◽  
Fancong Meng ◽  
Roderik Lindenbergh ◽  
Linh Truong-Hong ◽  
Bijun Li
Keyword(s):  
Neural Network ◽  
Laser Scanning ◽  
Large Scale ◽  
Reference Data ◽  
Point Clouds ◽  
Autonomous Driving ◽  
Place Recognition ◽  
Global Features ◽  
Mobile Mapping ◽  
Mapping Point

Mobile laser scanning (MLS) systems are often used to efficiently acquire reference data covering a large-scale scene. The terrestrial laser scanner (TLS) can easily collect high point density data of local scene. Localization of static TLS scans in mobile mapping point clouds can afford detailed geographic information for many specific tasks especially in autonomous driving and robotics. However, large-scale MLS reference data often have a huge amount of data and many similar scene data; significant differences may exist between MLS and TLS data. To overcome these challenges, this paper presents a novel deep neural network-based localization method in urban environment, divided by place recognition and pose refinement. Firstly, simple, reliable primitives, cylinder-like features were extracted to describe the global features of a local urban scene. Then, a probabilistic framework is applied to estimate a similarity between TLS and MLS data, under a stable decision-making strategy. Based on the results of a place recognition, we design a patch-based convolution neural network (CNN) (point-based CNN is used as kernel) for pose refinement. The input data unit is the batch consisting of several patches. One patch goes through three main blocks: feature extraction block (FEB), the patch correspondence search block and the pose estimation block. Finally, a global refinement was proposed to tune the predicted transformation parameters to realize localization. The research aim is to find the most similar scene of MLS reference data compared with the local TLS scan, and accurately estimate the transformation matrix between them. To evaluate the performance, comprehensive experiments were carried out. The experiments demonstrate that the proposed method has good performance in terms of efficiency, i.e., the runtime of processing a million points is 5 s, robustness, i.e., the success rate of place recognition is 100% in the experiments, accuracy, i.e., the mean rotation and translation error is (0.24 deg, 0.88 m) and (0.03 deg, 0.06 m) on TU Delft campus and Shanghai urban datasets, respectively, and outperformed some commonly used methods (e.g., iterative closest point (ICP), coherent point drift (CPD), random sample consensus (RANSAC)-based method).

scholarly journals Measuring the Contribution of Leaves to the Structural Complexity of Urban Tree Crowns with Terrestrial Laser Scanning

2021 ◽  
Vol 13 (14) ◽  
pp. 2773
Author(s):  
Georgios Arseniou ◽  
David W. MacFarlane ◽  
Dominik Seidel
Keyword(s):  
Laser Scanning ◽  
Structural Complexity ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
Urban Trees ◽  
Box Dimension ◽  
State University ◽  
Leaf Removal ◽  
Quercus Macrocarpa ◽  
Methodological Considerations

Trees have a fractal-like branching architecture that determines their structural complexity. We used terrestrial laser scanning technology to study the role of foliage in the structural complexity of urban trees. Forty-five trees of three deciduous species, Gleditsia triacanthos, Quercus macrocarpa, Metasequoia glyptostroboides, were sampled on the Michigan State University campus. We studied their structural complexity by calculating the box-dimension (Db) metric from point clouds generated for the trees using terrestrial laser scanning, during the leaf-on and -off conditions. Furthermore, we artificially defoliated the leaf-on point clouds by applying an algorithm that separates the foliage from the woody material of the trees, and then recalculated the Db metric. The Db of the leaf-on tree point clouds was significantly greater than the Db of the leaf-off point clouds across all species. Additionally, the leaf removal algorithm introduced bias to the estimation of the leaf-removed Db of the G. triacanthos and M. glyptostroboides trees. The index capturing the contribution of leaves to the structural complexity of the study trees (the ratio of the Db of the leaf-on point clouds divided by the Db of the leaf-off point clouds minus one), was negatively correlated with branch surface area and different metrics of the length of paths through the branch network of the trees, indicating that the contribution of leaves decreases as branch network complexity increases. Underestimation of the Db of the G. triacanthos trees, after the artificial leaf removal, was related to maximum branch order. These results enhance our understanding of tree structural complexity by disentangling the contribution of leaves from that of the woody structures. The study also highlighted important methodological considerations for studying tree structure, with and without leaves, from laser-derived point clouds.

scholarly journals Planar surface segmentation using a color-enhanced hybrid model for RGB-D camera-based indoor mobile mapping point clouds

2014 ◽  
Vol II-1 ◽  
pp. 61-67
Author(s):  
C. Wen ◽  
S. Lin ◽  
C. Wang ◽  
J. Li
Keyword(s):  
Point Cloud ◽  
Evaluation Process ◽  
Point Clouds ◽  
Planar Surface ◽  
Mobile Mapping ◽  
Hybrid Features ◽  
Segmentation Evaluation ◽  
Cloud Data ◽  
Surface Segmentation ◽  
Mapping Point

Point clouds acquired by RGB-D camera-based indoor mobile mapping system suffer the problems of being noisy, exhibiting an uneven distribution, and incompleteness, which are the problems that introduce difficulties for point cloud planar surface segmentation. This paper presents a novel color-enhanced hybrid planar surface segmentation model for RGB-D camera-based indoor mobile mapping point clouds based on region growing method, and the model specially addresses the planar surface extraction task over point cloud according to the noisy and incomplete indoor mobile mapping point clouds. The proposed model combines the color moments features with the curvature feature to select the seed points better. Additionally, a more robust growing criteria based on the hybrid features is developed to avoid the generation of excessive over-segmentation debris. A segmentation evaluation process with a small set of labeled segmented data is used to determine the optimal hybrid weight. Several comparative experiments were conducted to evaluate the segmentation model, and the experimental results demonstrate the effectiveness and efficiency of the proposed hybrid segmentation method for indoor mobile mapping three-dimensional (3D) point cloud data.

scholarly journals Mapping for Indoor Walking Path Using Mobile Laser Scanning

2020 ◽  
Vol 17 (3) ◽  
pp. 43
Author(s):  
Nurfadhilah Ruslan ◽  
Nur Syazwani Rosadlan ◽  
Nabilah Naharudin ◽  
Zulkiflee Abd Latif
Keyword(s):  
Point Cloud ◽  
Indoor Environment ◽  
Laser Scanning ◽  
Point Clouds ◽  
Advance Technology ◽  
Sustainable City ◽  
Outdoor Walking ◽  
Walking Environment ◽  
Mapping Point ◽  
Building Elements

Walkability is one of the keys in developing a sustainable city. These days, many cities have considered enhancing walkability for pedestrian paths to ensure the seamless walking experience for people to reach their destination. Therefore, it is very important to have a good walking environment so people will find walking pleasant. However, there was a lack of studies attempting to include indoor walking environments in their walkability analysis. Most of them only consider outdoor walking paths. This might be due to the difficulties in modelling the indoor walking environment. With the advance technology of laser scanning, it might be possible to develop an indoor walking path by using point clouds collected for a building. The usage of point clouds could make it easier to segment the building elements and obstacles in an indoor environment. In order to produce an indoor map, it is important to reconstruct the building elements such as wall, ceiling, window and door. Therefore, this paper aims to generate the indoor walking path using laser scanning point clouds showing all the options to the pedestrians.Keywords: Walkability, indoor mapping, point cloud, laser scanning, mobile laser scanning

A new drone laser scanning benchmark dataset for characterization of single-tree and forest biophysical properties

2021 ◽  
Author(s):  
Puliti Stefano ◽  
Grant D. Pears ◽  
Michael S. Watt ◽  
Edward Mitchard ◽  
Iain McNicol ◽  
...  
Keyword(s):  
Boreal Forests ◽  
Laser Scanning ◽  
Method Development ◽  
Three Dimensional ◽  
Point Clouds ◽  
Benchmark Dataset ◽  
Validation Dataset ◽  
Tree Structures ◽  
Single Tree ◽  
Individual Trees

<p>Survey-grade drone laser scanners suitable for unmanned aerial vehicles (UAV-LS) allow the efficient collection of finely detailed three-dimensional information of tree structures. This data type allows forests to be resolved into discrete individual trees and has shown promising results in providing accurate in-situ observations of key forestry variables. New and improved approaches for analyzing UAV-LS point clouds have to be developed to transform the vast amounts of data from UAV-LS into actionable insights and decision support. Many different studies have explored various methods for automating single tree detection, segmentation, parsing into different tree components, and measurement of biophysical variables (e.g., diameter at breast height). Despite the considerable efforts dedicated to developing automated ways to process UAV-LS data into useful data, current methods tend to be tailored to small datasets, and it remains challenging to evaluate the performance of different algorithms based on a consistent validation dataset. To fill this knowledge gap and to further advance our ability to measure forests from UAV-LS data, we present a new benchmarking dataset. This data is composed of manually labelled UAV-LS data acquired a number of continents and biomes which span tropical to boreal forests. The UAV-LS data was collected exclusively used survey-grade sensors such as the Riegl VUX and mini-VUX series which are characterized by a point density in the range 1 – 10 k points m<sup>2</sup>. Currently, such data represent the state-of-the-art in aerial laser scanning data. The benchmark data consists of a library of single-tree point clouds, aggregated to sample plots, with each point classified as either stem, branch, or leaves. With the objective of releasing such a benchmark dataset as a public asset, in the future, researchers will be able to leverage such pre-existing labelled trees for developing new methods to measure forests from UAV-LS data. The availability of benchmarking datasets represents an important driver for enabling the development of robust and accurate methods. Such a benchmarking dataset will also be important for a consistent comparison of existing or future algorithms which will guide future method development.</p>

scholarly journals Segmentation of LiDAR Data Using Multilevel Cube Code

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
So-Young Park ◽  
Dae Geon Lee ◽  
Eun Jin Yoo ◽  
Dong-Cheon Lee
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Lidar Data ◽  
Chain Code ◽  
Cloud Data ◽  
Airborne Laser ◽  
Wide Range ◽  
Scanning Systems

Light detection and ranging (LiDAR) data collected from airborne laser scanning systems are one of the major sources of spatial data. Airborne laser scanning systems have the capacity for rapid and direct acquisition of accurate 3D coordinates. Use of LiDAR data is increasing in various applications, such as topographic mapping, building and city modeling, biomass measurement, and disaster management. Segmentation is a crucial process in the extraction of meaningful information for applications such as 3D object modeling and surface reconstruction. Most LiDAR processing schemes are based on digital image processing and computer vision algorithms. This paper introduces a shape descriptor method for segmenting LiDAR point clouds using a “multilevel cube code” that is an extension of the 2D chain code to 3D space. The cube operator segments point clouds into roof surface patches, including superstructures, removes unnecessary objects, detects the boundaries of buildings, and determines model key points for building modeling. Both real and simulated LiDAR data were used to verify the proposed approach. The experiments demonstrated the feasibility of the method for segmenting LiDAR data from buildings with a wide range of roof types. The method was found to segment point cloud data effectively.

Feature selection for quality assessment of indoor mobile mapping point clouds

2016 ◽  
Author(s):  
Fangfang Huang ◽  
Chenglu Wen ◽  
Cheng Wang ◽  
Jonathan Li
Keyword(s):  
Feature Selection ◽  
Quality Assessment ◽  
Point Clouds ◽  
Mobile Mapping ◽  
Selection For ◽  
Mapping Point

scholarly journals Geoarchaeological site documentation and analysis of 3D data derived by terrestrial laser scanning

2014 ◽  
Vol II-5 ◽  
pp. 173-179 ◽  
Author(s):  
D. Hoffmeister ◽  
S. Zellmann ◽  
K. Kindermann ◽  
A. Pastoors ◽  
U. Lang ◽  
...  
Keyword(s):  
Cross Sections ◽  
Laser Scanning ◽  
Laser Scanner ◽  
Terrestrial Laser Scanning ◽  
Point Clouds ◽  
3D Data ◽  
Research Problems ◽  
Survey Results ◽  
Rtk Gps ◽  
Derived Data

Terrestrial laser scanning was conducted to document and analyse sites of geoarchaeological interest in Jordan, Egypt and Spain. In those cases, the terrestrial laser scanner LMS-Z420i from Riegl was used in combination with an accurate RTK-GPS for georeferencing of the point clouds. Additionally, local surveying networks were integrated by established transformations and used for indirect registration purposes. All data were integrated in a workflow that involves different software and according results. The derived data were used for the documentation of the sites by accurate plans and cross-sections. Furthermore, the 3D data were analysed for geoarchaeological research problems, such as volumetric determinations, the ceiling thickness of a cave and lighting simulations based on path tracing. The method was reliable in harsh environmental conditions, but the weight of the instrument, the measuring time and the minimum measurement distance were a drawback. However, generally an accurate documentation of the sites was possible. Overall, the integration in a 3D GIS is easily possible by the accurate georeference of the derived data. In addition, local survey results are also implemented by the established transformations. Enhanced analyses based on the derived 3D data shows promising results.

scholarly journals AN AUTOMATIC ALGORITHM FOR MINIMIZING ANOMALIES AND DISCREPANCIES IN POINT CLOUDS ACQUIRED BY LASER SCANNING TECHNIQUE

2016 ◽  
Vol XLI-B5 ◽  
pp. 779-783
Author(s):  
Fabiane Bordin ◽  
Luiz Gonzaga Jr ◽  
Fabricio Galhardo Muller ◽  
Mauricio Roberto Veronez ◽  
Marco Scaioni
Keyword(s):  
Laser Pulse ◽  
Laser Beam ◽  
Laser Scanning ◽  
Point Clouds ◽  
Intrinsic Properties ◽  
Estimation Errors ◽  
Scanning Technique ◽  
3D Data ◽  
Laser Pulse Intensity ◽  
Automatic Algorithm

Laser scanning technique from airborne and land platforms has been largely used for collecting 3D data in large volumes in the field of geosciences. Furthermore, the laser pulse intensity has been widely exploited to analyze and classify rocks and biomass, and for carbon storage estimation. In general, a laser beam is emitted, collides with targets and only a percentage of emitted beam returns according to intrinsic properties of each target. Also, due interferences and partial collisions, the laser return intensity can be incorrect, introducing serious errors in classification and/or estimation processes. To address this problem and avoid misclassification and estimation errors, we have proposed a new algorithm to correct return intensity for laser scanning sensors. Different case studies have been used to evaluate and validated proposed approach.

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