scholarly journals Open software and standards in the realm of laser scanning technology

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Francesco Pirotti
Keyword(s):  
Open Source ◽  
Laser Scanning ◽  
State Of The Art ◽  
Data Access ◽  
Point Clouds ◽  
Lidar Data ◽  
Data Standards ◽  
Web Based ◽  
Massive Point ◽  
Valid Solution

AbstractThis review aims at introducing laser scanning technology and providing an overview of the contribution of open source projects for supporting the utilization and analysis of laser scanning data. Lidar technology is pushing to new frontiers in mapping and surveying topographic data. The open source community has supported this by providing libraries, standards, interfaces, modules all the way to full software. Such open solutions provide scientists and end-users valuable tools to access and work with lidar data, fostering new cutting-edge investigation and improvements of existing methods.The first part of this work provides an introduction on laser scanning principles, with references for further reading. It is followed by sections respectively reporting on open standards and formats for lidar data, tools and finally web-based solutions for accessing lidar data. It is not intended to provide a thorough review of state of the art regarding lidar technology itself, but to provide an overview of the open source toolkits available to the community to access, visualize, edit and process point clouds. A range of open source features for lidar data access and analysis is provided, providing an overview of what can be done with alternatives to commercial end-to-end solutions. Data standards and formats are also discussed, showing what are the challenges for storing and accessing massive point clouds.The desiderata are to provide scientists that have not yet worked with lidar data an overview of how this technology works and what open source tools can be a valid solution for their needs in analysing such data. Researchers that are already involved with lidar data will hopefully get ideas on integrating and improving their workflow through open source solutions.

scholarly journals CHANGE DETECTION AND DEFORMATION ANALYSIS BASED ON MOBILE LASER SCANNING DATA OF URBAN AREAS

2020 ◽  
Vol V-2-2020 ◽  
pp. 703-710
Author(s):  
J. Gehrung ◽  
M. Hebel ◽  
M. Arens ◽  
U. Stilla
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Laser Scanning ◽  
State Of The Art ◽  
Point Clouds ◽  
Deformation Analysis ◽  
Lidar Data ◽  
Efficient Manner ◽  
Incomplete Observations ◽  
Novel Method

Abstract. Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %.

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.

scholarly journals Transparent exploration of machine learning for biomarker discovery from proteomics and omics data

2021 ◽  
Author(s):  
Furkan M. Torun ◽  
Sebastian Virreira Winter ◽  
Sophia Doll ◽  
Felix M. Riese ◽  
Artem Vorobyev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Biomarker Discovery ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Open Science ◽  
Easy Access ◽  
Health State ◽  
List Type ◽  
Web Based

AbstractBiomarkers are of central importance for assessing the health state and to guide medical interventions and their efficacy, but they are lacking for most diseases. Mass spectrometry (MS)-based proteomics is a powerful technology for biomarker discovery, but requires sophisticated bioinformatics to identify robust patterns. Machine learning (ML) has become indispensable for this purpose, however, it is sometimes applied in an opaque manner, generally requires expert knowledge and complex and expensive software. To enable easy access to ML for biomarker discovery without any programming or bioinformatic skills, we developed ‘OmicLearn’ (https://OmicLearn.com), an open-source web-based ML tool using the latest advances in the Python ML ecosystem. We host a web server for the exploration of the researcher’s results that can readily be cloned for internal use. Output tables from proteomics experiments are easily uploaded to the central or a local webserver. OmicLearn enables rapid exploration of the suitability of various ML algorithms for the experimental datasets. It fosters open science via transparent assessment of state-of-the-art algorithms in a standardized format for proteomics and other omics sciences.Graphical AbstractHighlightsOmicLearn is an open-source platform allows researchers to apply machine learning (ML) for biomarker discoveryThe ready-to-use structure of OmicLearn enables accessing state-of-the-art ML algorithms without requiring any prior bioinformatics knowledgeOmicLearn’s web-based interface provides an easy-to-follow platform for classification and gaining insights into the datasetSeveral algorithms and methods for preprocessing, feature selection, classification and cross-validation of omics datasets are integratedAll results, settings and method text can be exported in publication-ready formats

OpenBIM Framework for a Collaborative Historic Preservation System

2016 ◽  
Vol 5 (4) ◽  
pp. 1-11 ◽  
Author(s):  
Shawn E. O'Keeffe
Keyword(s):  
Historic Preservation ◽  
Open Source ◽  
Laser Scanning ◽  
3D Models ◽  
System Framework ◽  
Web Based ◽  
Industry Foundation Classes ◽  
Open Standard ◽  
Open Standards ◽  
Development And Management

The authors have developed a novel system framework for a historic preservation system utilising open standards and open source tools. The framework enables the integration of open standard 3D models and GIS in a virtual environment (VE). It also allows the storage and harvesting of data via an open source web-based central repository. The framework is designed for bi-directional interoperability when utilised for monument tracking, preservation, conservation, re-excavation, etc. To date, no such system framework exists for the development and management of historic monuments using open standards e57 for terrestrial laser scanning (TLS) data, the industry foundation classes (IFC) for 3D semantic rich models, and CityGML for integrating IFC and GIS in a VE. The web-based VE is accessed from the BimServer and envisaged for monitoring monument erosion, movement, and damage to monuments by human activity or nature, collaboration between different industry actors to share knowledge, experience, and expertise globally, etc.

scholarly journals GENERATING SYNTHETIC 3D POINT SEGMENTS FOR IMPROVED CLASSIFICATION OF MOBILE LIDAR POINT CLOUDS

2021 ◽  
Vol XLIII-B2-2021 ◽  
pp. 139-144
Author(s):  
S. A. Chitnis ◽  
Z. Huang ◽  
K. Khoshelham
Keyword(s):  
Semantic Information ◽  
State Of The Art ◽  
Point Clouds ◽  
Classification Performance ◽  
Training Data ◽  
Lidar Data ◽  
Real Point ◽  
The Road ◽  
Training Samples

Abstract. Mobile lidar point clouds are commonly used for 3d mapping of road environments as they provide a rich, highly detailed geometric representation of objects on and around the road. However, raw lidar point clouds lack semantic information about the type of objects, which is necessary for various applications. Existing methods for the classification of objects in mobile lidar data, including state of the art deep learning methods, achieve relatively low accuracies, and a primary reason for this under-performance is the inadequacy of available 3d training samples to sufficiently train deep networks. In this paper, we propose a generative model for creating synthetic 3d point segments that can aid in improving the classification performance of mobile lidar point clouds. We train a 3d Adversarial Autoencoder (3dAAE) to generate synthetic point segments that exhibit a high resemblance to and share similar geometric features with real point segments. We evaluate the performance of a PointNet-like classifier trained with and without the synthetic point segments. The evaluation results support our hypothesis that training a classifier with training data augmented with synthetic samples leads to significant improvement in the classification performance. Specifically, our model achieves an F1 score of 0.94 for vehicles and pedestrians and 1.00 for traffic signs.

scholarly journals DETERMINING GEOMETRIC PARAMETERS OF AGRICULTURAL TREES FROM LASER SCANNING DATA OBTAINED WITH UNMANNED AERIAL VEHICLE

2018 ◽  
Vol XLII-2 ◽  
pp. 407-410 ◽  
Author(s):  
E. Hadas ◽  
G. Jozkow ◽  
A. Walicka ◽  
A. Borkowski
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Laser Scanning ◽  
Principal Component ◽  
Point Clouds ◽  
Apple Orchard ◽  
Lidar Data ◽  
Local Minima ◽  
Alpha Shape ◽  
Aerial Vehicle ◽  
Individual Trees

The estimation of dendrometric parameters has become an important issue for agriculture planning and for the efficient management of orchards. Airborne Laser Scanning (ALS) data is widely used in forestry and many algorithms for automatic estimation of dendrometric parameters of individual forest trees were developed. Unfortunately, due to significant differences between forest and fruit trees, some contradictions exist against adopting the achievements of forestry science to agricultural studies indiscriminately.<br> In this study we present the methodology to identify individual trees in apple orchard and estimate heights of individual trees, using high-density LiDAR data (3200&amp;thinsp;points/m<sup>2</sup>) obtained with Unmanned Aerial Vehicle (UAV) equipped with Velodyne HDL32-E sensor. The processing strategy combines the alpha-shape algorithm, principal component analysis (PCA) and detection of local minima. The alpha-shape algorithm is used to separate tree rows. In order to separate trees in a single row, we detect local minima on the canopy profile and slice polygons from alpha-shape results. We successfully separated 92&amp;thinsp;% of trees in the test area. 6&amp;thinsp;% of trees in orchard were not separated from each other and 2&amp;thinsp;% were sliced into two polygons. The RMSE of tree heights determined from the point clouds compared to field measurements was equal to 0.09&amp;thinsp;m, and the correlation coefficient was equal to 0.96. The results confirm the usefulness of LiDAR data from UAV platform in orchard inventory.

scholarly journals Mobile Laser Scanned Point-Clouds for Road Object Detection and Extraction: A Review.

2021 ◽  
Author(s):  
Lingfei Ma ◽  
Ying Li ◽  
Jonathan Li ◽  
Cheng Wang ◽  
Ruisheng Wang ◽  
...  
Keyword(s):  
Object Detection ◽  
Laser Scanning ◽  
State Of The Art ◽  
Three Dimensional ◽  
Point Clouds ◽  
Extraction Methods ◽  
The State ◽  
Integrated Analysis ◽  
High Definition ◽  
Accurate Localization

The mobile laser scanning (MLS) technique has attracted considerable attention for providing high-density, high-accuracy, unstructured, three-dimensional (3D) geo-referenced point-cloud coverage of the road environment. Recently, there has been an increasing number of applications of MLS in the detection and extraction of urban objects. This paper presents a systematic review of existing MLS related literature. This paper consists of three parts. Part 1 presents a brief overview of the state-of-the-art commercial MLS systems. Part 2 provides a detailed analysis of on-road and off-road information inventory methods, including the detection and extraction of on-road objects (e.g., road surface, road markings, driving lines, and road crack) and off-road objects (e.g., pole-like objects and power lines). Part 3 presents a refined integrated analysis of challenges and future trends. Our review shows that MLS technology is well proven in urban object detection and extraction, since the improvement of hardware and software accelerate the efficiency and accuracy of data collection and processing. When compared to other review papers focusing on MLS applications, we review the state-of-the-art road object detection and extraction methods using MLS data and discuss their performance and applicability. The main contribution of this review demonstrates that the MLS systems are suitable for supporting road asset inventory, ITS-related applications, high-definition maps, and other highly accurate localization services.

scholarly journals EFFICIENT LOADING AND VISUALIZATION OF MASSIVE FEATURE-RICH POINT CLOUDS WITHOUT HIERARCHICAL ACCELERATION STRUCTURES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 293-300
Author(s):  
J. Otepka ◽  
G. Mandlburger ◽  
M. Schütz ◽  
N. Pfeifer ◽  
M. Wimmer
Keyword(s):  
Open Source ◽  
Laser Scanning ◽  
Point Clouds ◽  
Surface Generation ◽  
Data Sets ◽  
Spatial Indexing ◽  
Structure Generation ◽  
Standard Product ◽  
Depth Analysis ◽  
Point Data

Abstract. Nowadays, point clouds are the standard product when capturing reality independent of scale and measurement technique. Especially, Dense Image Matching (DIM) and Laser Scanning (LS) are state of the art capturing methods for a great variety of applications producing detailed point clouds up to billions of points. In-depth analysis of such huge point clouds typically requires sophisticated spatial indexing structures to support potentially long-lasting automated non-interactive processing tasks like feature extraction, semantic labelling, surface generation, and the like. Nevertheless, a visual inspection of the point data is often necessary to obtain an impression of the scene, roughly check for completeness, quality, and outlier rates of the captured data in advance. Also intermediate processing results, containing additional per-point computed attributes, may require visual analyses to draw conclusions or to parameterize further processing. Over the last decades a variety of commercial, free, and open source viewers have been developed that can visualise huge point clouds and colorize them based on available attributes. However, they have either a poor loading and navigation performance, visualize only a subset of the points, or require the creation of spatial indexing structures in advance. In this paper, we evaluate a progressive method that is capable of rendering any point cloud that fits in GPU memory in real time without the need of time consuming hierarchical acceleration structure generation. In combination with our multi-threaded LAS and LAZ loaders, we achieve load performance of up to 20 million points per second, display points already while loading, support flexible switching between different attributes, and rendering up to one billion points with visually appealing navigation behaviour. Furthermore, loading times of different data sets for different open source and commercial software packages are analysed.

scholarly journals UAV LIDAR DATA PROCESSING: INFLUENCE OF FLIGHT HEIGHT ON GEOMETRIC ACCURACY, RADIOMETRIC INFORMATION AND PARAMETER SETTING IN DTM PRODUCTION

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 21-26
Author(s):  
K. Bakuła ◽  
M. Pilarska ◽  
W. Ostrowski ◽  
A. Nowicki ◽  
Z. Kurczyński
Keyword(s):  
Laser Scanning ◽  
Interpolation Method ◽  
Laser Scanner ◽  
Point Clouds ◽  
Lidar Data ◽  
Radiometric Calibration ◽  
Statistical Parameters ◽  
Flight Height ◽  
Vertical Error ◽  
The Impact

Abstract. This article presents the results of studies related to the impact of flight altitude of UAV equipped with lidar data on geometric and radiometric information. Experiments were conducted in two test areas by performing UAV test flight missions at different UAV Laser Scanner (ULS) altitudes. The results were compared to other parameters describing the point clouds in order to answer the questions related to their genesis and evaluation of a product from such high-resolution datasets. The accuracy of the elevation models was assessed on the basis of control points measured with GNSS RTK and Terrestrial Laser Scanning (TLS). Accuracy was assessed by statistical parameters and differential digital elevation models. The second issue raised in this work is the study of the decrease in radiometric value with an increase in platform elevation. The results of this work clearly indicate the very low impact of platform altitude on DTM vertical error. In presented works the suggestion about DTM resolution and interpolation method are provided. Moreover, the influence of flight height on the reflectance and intensity is notable, however, its impact is related more with the details and resolution of the raster than radiometric values considering the possibility of radiometric calibration of the intensity.

scholarly journals A Concealed Car Extraction Method Based on Full-Waveform LiDAR Data

2016 ◽  
Vol 2016 ◽  
pp. 1-12
Author(s):  
Chuanrong Li ◽  
Mei Zhou ◽  
Menghua Liu ◽  
Lian Ma ◽  
Jinhu Wang
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Lidar Data ◽  
Full Waveform ◽  
Point Cloud Segmentation ◽  
Sample Data ◽  
Waveform Lidar ◽  
Occlusion Effect ◽  
Full Waveform Lidar ◽  
Feature Pattern

Concealed cars extraction from point clouds data acquired by airborne laser scanning has gained its popularity in recent years. However, due to the occlusion effect, the number of laser points for concealed cars under trees is not enough. Thus, the concealed cars extraction is difficult and unreliable. In this paper, 3D point cloud segmentation and classification approach based on full-waveform LiDAR was presented. This approach first employed the autocorrelationGcoefficient and the echo ratio to determine concealed cars areas. Then the points in the concealed cars areas were segmented with regard to elevation distribution of concealed cars. Based on the previous steps, a strategy integrating backscattered waveform features and the view histogram descriptor was developed to train sample data of concealed cars and generate the feature pattern. Finally concealed cars were classified by pattern matching. The approach was validated by full-waveform LiDAR data and experimental results demonstrated that the presented approach can extract concealed cars with accuracy more than 78.6% in the experiment areas.

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