scholarly journals THE CONCEPT OF LIDAR DATA QUALITY ASSESSMENT IN THE CONTEXT OF BIM MODELING

2019 ◽  
Vol XLII-1/W2 ◽  
pp. 61-66 ◽  
Author(s):  
A. Warchoł
Keyword(s):  
Spatial Data ◽  
Point Clouds ◽  
Lidar Data ◽  
Data Sets ◽  
Density Parameter ◽  
Large Area ◽  
3D Data ◽  
Data Quality Assessment ◽  
Other Information ◽  
Architectural Detail

<p><strong>Abstract.</strong> LiDAR technology has revolutionized the area of 3D data acquisition. It is possible to obtain in a very fast and accurate way geometric and other information for a large area . Along with the development of design technology, LiDAR point clouds are often used to create inventory models of building objects and installations. This paper presents the possibilities of assessing LiDAR data for BIM modeling. The areas in which the assessment and description of obtained TLS data is important are presented. In addition to the attributes for assessing the quality of spatial data contained in the ISO 19157 standard, a density parameter was proposed. Examples of point clouds with different density for the architectural detail are presented in the final part of the work. For the attributes describing LiDAR data sets the levels of importance have been proposed for.</p>

scholarly journals The Highest Peaks of the Mountains: Comparing the Use of GNSS, LiDAR Point Clouds, DTMs, Databases, Maps, and Historical Sources

Energies ◽  
2021 ◽  
Vol 14 (18) ◽  
pp. 5731
Author(s):  
Stanisław Szombara ◽  
Marta Róg ◽  
Krystian Kozioł ◽  
Kamil Maciuk ◽  
Bogdan Skorupa ◽  
...  
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Point Clouds ◽  
Satellite System ◽  
Data Sources ◽  
Data Sets ◽  
Large Area ◽  
Historical Sources ◽  
Spatial Data Sets ◽  
Gnss Measurements

Advances in remote data acquisition techniques have contributed to the flooding of society with spatial data sets and information. Widely available spatial data sets, including digital terrain models (DTMs) from aerial laser scanning (ALS) data, are finding more and more new applications. The article analyses and compares the heights of the 14 highest peaks of the Polish Carpathians derived from different data sources. Global navigation satellite system (GNSS) geodetic measurements were used as reference. The comparison primarily involves ALS data, and selected peaks’ GNSS measurements carried out with Xiaomi Mi 8 smartphones were also compared. Recorded raw smartphone GNSS measurements were used for calculations in post-processing mode. Other data sources were, among others, global and local databases and models and topographic maps (modern and old). The article presents an in-depth comparison of Polish and Slovak point clouds for two peaks. The results indicate the possible use of large-area laser scanning in determining the maximum heights of mountain peaks and the need to use geodetic GNSS measurements for selected peaks. For the Polish peak of Rysy, the incorrect classification of point clouds causes its height to be overestimated. The conclusions presented in the article can be used in the dissemination of knowledge and to improve positioning methods.

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

2015 ◽  
Vol II-3/W4 ◽  
pp. 263-270 ◽  
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.

scholarly journals THE PHIL-LIDAR 2 PROGRAM: NATIONAL RESOURCE INVENTORY OF THE PHILIPPINES USING LIDAR AND OTHER REMOTELY SENSED DATA

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1123-1127 ◽  
Author(s):  
A. C. Blanco ◽  
A. M. Tamondong ◽  
A. M. C. Perez ◽  
M. R. C. O. Ang ◽  
E. C. Paringit
Keyword(s):  
Image Analysis ◽  
Warning System ◽  
Point Clouds ◽  
The Philippines ◽  
Lidar Data ◽  
Data Sets ◽  
Mentoring Program ◽  
Remotely Sensed Data ◽  
Resource Inventory ◽  
Parameter Values

The Philippines embarked on a nationwide mapping endeavour through the Disaster Risk and Exposure Assessment for Mitigation (DREAM) Program of the University of the Philippines and the Department of Science and Technology (DOST). The derived accurate digital terrain models (DTMs) are used in flood models to generate risk maps and early warning system. With the availability of LiDAR data sets, the Phil-LiDAR 2 program was conceptualized as complementary to existing programs of various national government agencies and to assist local government units. Phil-LiDAR 2 aims to provide an updated natural resource inventory as detailed as possible using LiDAR point clouds, LiDAR derivative products, orthoimages and other RS data. The program assesses the following natural resources over a period of three years from July 2014: agricultural, forest, coastal, water, and renewable energy. To date, methodologies for extracting features from LiDAR data sets have been developed. The methodologies are based on a combination of object-based image analysis, pixel-based image analysis, optimization of feature selection and parameter values, and field surveys. One of the features of the Phil-LiDAR 2 program is the involvement of fifteen (15) universities throughout the country. Most of these do not have prior experience in remote sensing and mapping. With such, the program has embarked on a massive training and mentoring program. The program is producing more than 200 young RS specialists who are protecting the environment through RS and other geospatial technologies. This paper presents the program, the methodologies so far developed, and the sample outputs.

scholarly journals Big LIDAR Data in Digital Earth: Ways Out of Dead End

2020 ◽  
pp. paper46-1-paper46-10
Author(s):  
Ilya Rylskiy
Keyword(s):  
Data Storage ◽  
Spatial Data ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Alternative Methods ◽  
Measuring Instruments ◽  
Lidar Data ◽  
Data Sets ◽  
Spatial Data Sets ◽  
Limited Precision

During past 25 years, laser scanning has evolved from an experimental method into a fully autonomous family of Earth remote sensing methods. Now this group of methods provides the most accurate and detailed spatial data sets, while the cost of data is constantly falling, the number of measuring instruments (laser scanners) is constantly growing. The volumes of data that will be obtained during the surveys in the coming decades will allow the creation of the first sub-global coverage of the planet. However, the flip side of high accuracy and detail is the need to store fantastically large volumes of three-dimensional data without loss of accuracy. At the same time, the ability to work with the specified data in both 2D and 3D mode should be improved. Standard storage methods (file method, geodatabases, archiving, etc) solve the problem only partially. At the same time, there are some other alternative methods that can remove current restrictions and lead to the emergence of more flexible and functional spatial data infrastructures. One of the most flexible and promising ways of laser data storage and processing are quadtree and octree-based approaches. Of course, these approaches are more complicated than typical file data structures, that are commonly used for LIDAR data storage, but they allow users to solve some typical negative features of point datasets (processing speed, non-topological spatial structure, limited precision, etc.).

scholarly journals WebGL Visualisation of 3D Environmental Models Based on Finnish Open Geospatial Data Sets

2014 ◽  
Vol XL-3 ◽  
pp. 163-169 ◽  
Author(s):  
A. Krooks ◽  
J. Kahkonen ◽  
L. Lehto ◽  
P. Latvala ◽  
M. Karjalainen ◽  
...  
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
National Level ◽  
Image Data ◽  
Point Clouds ◽  
Open Pit ◽  
Data Sets ◽  
Environmental Models ◽  
3D City Model ◽  
Recent Developments

Recent developments in spatial data infrastructures have enabled real time GIS analysis and visualization using open input data sources and service interfaces. In this study we present a new concept where metric point clouds derived from national open airborne laser scanning (ALS) and photogrammetric image data are processed, analyzed, finally visualised a through open service interfaces to produce user-driven analysis products from targeted areas. The concept is demonstrated in three environmental applications: assessment of forest storm damages, assessment of volumetric changes in open pit mine and 3D city model visualization. One of the main objectives was to study the usability and requirements of national level photogrammetric imagery in these applications. The results demonstrated that user driven 3D geospatial analyses were possible with the proposed approach and current technology, for instance, the landowner could assess the amount of fallen trees within his property borders after a storm easily using any web browser. On the other hand, our study indicated that there are still many uncertainties especially due to the insufficient standardization of photogrammetric products and processes and their quality indicators.

scholarly journals DEVELOPMENT OF VIRTUAL REALITY APPLICATION FOR CULTURAL HERITAGE VISUALIZATION FROM MULTI-SOURCE 3D DATA

2019 ◽  
Vol XLII-2/W9 ◽  
pp. 261-267 ◽  
Author(s):  
K. Choromański ◽  
J. Łobodecki ◽  
K. Puchała ◽  
W. Ostrowski
Keyword(s):  
Virtual Reality ◽  
Cultural Heritage ◽  
Spatial Data ◽  
Experimental Approach ◽  
Point Clouds ◽  
3D Models ◽  
3D Data ◽  
Wide Range ◽  
Heritage Visualization ◽  
Virtual Reality Application

<p><strong>Abstract.</strong> Immersive technologies like Virtual or Augmented Reality (VR/AR) are lately becoming more and more popular in wide range of scientific applications. These technologies provide the most immersive way to present spatial data such as point clouds or 3D models. This type of solutions also has significant potential for virtual presentation of cultural heritage. Combination of high-quality photogrammetric 3D models, virtual reality technologies and an advanced visualization engine may bring effect in the form of a nearly real-world experience which may be very useful in terms of popularization as well as research in the area of cultural heritage. In this paper we would like to present results of experimental approach to establish VR system for the Museum of King John III’s Palace at Wilanów in Warsaw, Poland.</p>

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.

Filtering LiDAR Data Using Binary Quadric Trend Surface and Morphological Approaches

2012 ◽  
Vol 594-597 ◽  
pp. 2361-2366 ◽  
Author(s):  
Feng Li ◽  
Xi Min Cui ◽  
Ling Zhang ◽  
Shu Wei Shan ◽  
Kun Lun Song
Keyword(s):  
Point Clouds ◽  
Experimental Results ◽  
Light Detection And Ranging ◽  
Lidar Data ◽  
Data Sets ◽  
Type I ◽  
Morphological Filter ◽  
Light Detection ◽  
Trend Surface ◽  
Sample Data

Automatically identifying and removing above-ground laser points from terrain surface is proved to be a challenging task for complicated and discontinuous scenarios. Eight methods have been developed and contrasted with each other for filtering LiDAR (Light Detection and Ranging) data. Only one approach is difficult to acquire high precisions for various landscapes. This paper presents a method filtering point clouds in which firstly a binary quadric trend surface is used to remove most non-terrain points by a defined height threshold and subsequently a progressive morphological filter further is employed to detect ground measurements. The experimental results demonstrate that this method yields less type I and total errors compared with other eight approaches based on ISPRS sample data sets.

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