scholarly journals A Model Study of Building Seismic Damage Information Extraction and Analysis on Ground-Based LiDAR Data

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Fan Yang ◽  
Xintao Wen ◽  
Xiaoshan Wang ◽  
Xiaoli Li ◽  
Zhiqiang Li
Keyword(s):  
High Precision ◽  
Laser Scanning ◽  
Full Range ◽  
Scientific Basis ◽  
Building Damage ◽  
Vector Model ◽  
Lidar Data ◽  
Emergency Relief ◽  
Cloud Data ◽  
Ground Based Lidar

Earthquake disasters can have a serious impact on people’s lives and property, with damage to buildings being one of the main causes of death and injury. A rapid assessment of the extent of building damage is essential for emergency response management, rescue operations, and reconstruction. Terrestrial laser scanning technology can obtain high precision light detection and ranging (LiDAR) point cloud data of the target. The technology is widely used in various fields; however, the quantitative analysis of building seismic information is the focus and difficulty of ground-based LiDAR data analysis processing. This paper takes full advantage of the high-precision characteristics of ground-based LiDAR data. A triangular network vector model (TIN-shaped model) was created in conjunction with the alpha shapes algorithm, solving the problem of small, nonvisually identifiable postearthquake building damage feature extraction bias. The model measures the length, width, and depth of building cracks, extracts the amount of wall tilt deformation, and labels the deformation zone. The creation of this model can provide scientific basis and technical support for postearthquake emergency relief, assessment of damage to buildings, extraction of deformation characteristics of other structures (bridges, tunnels, dams, etc.), and seismic reinforcement of buildings. The research data in this paper were collected by the author’s research team in the first time after the 2013 Lushan earthquake and is one of the few sets of foundation of LiDAR data covering the full range of postearthquake building types in the region, with the data information mainly including different damage levels of different structural types of buildings. The modeling analysis of this data provides a scientific basis for establishing the earthquake damage matrix of buildings in the region.

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 Assessing the Applicability of Mobile Laser Scanning for Mapping Forest Roads in the Republic of Korea

2020 ◽  
Vol 12 (9) ◽  
pp. 1502
Author(s):  
Hyeongkeun Kweon ◽  
Jung Il Seo ◽  
Joon-Woo Lee
Keyword(s):  
High Precision ◽  
Laser Scanning ◽  
Sustainable Forest Management ◽  
Low Cost ◽  
Unit Length ◽  
Environmental Damage ◽  
Positional Accuracy ◽  
Forest Roads ◽  
Forest Road ◽  
Cloud Data

Forest roads are an essential facility for sustainable forest management and protection. With advances in survey technology, such as Light Detection and Ranging, forest road maps with greater accuracy and resolution can be produced. This study produced a 3D map for establishment of a forest road inventory using a Mobile Laser Scanning (MLS) device mounted on a vehicle in four study forest roads in Korea, in order to review its precision, accuracy and efficiency based on comparisons with mapping using Total Station (TS) and Global Navigation Satellite System (GNSS). We counted the points that consist of the cloud data of the maps to determine the degree of precision density, and then compared this with 50 points at 20-m intervals on the centerlines bisecting the widths of the study forest roads. Then, we evaluated the relative positional accuracy of the MLS data based on three criteria: the total length of each forest road; the Root Mean Square Error (RMSE) obtained from coordinate values of the MLS and TS surveys compared to the GNSS survey; and the ratios of the centerlines extracted by the MLS and TS surveys overlaid to the buffer zone by the GNSS survey. Finally, we estimated the time and cost per unit length for producing the map to examine the efficiency of MLS mapping compared to the other two surveys. The results showed that the point cloud data acquired by the MLS survey on the study forest roads had very high precision and so is sufficient to produce a 3D forest road map with high-precision density and a low RMSE value. Although the equipment rental cost is somewhat high, the fact that information targeting on all spatial elements of forest roads can be obtained with a low cost of labor is a benefit when evaluating the efficiency of MLS survey and mapping. Our findings are expected to provide a quantitative assessment of both maintaining sustainable effectiveness and preventing potential environmental damage of forest roads.

scholarly journals Point Cloud Data Retrieval from 3D Geospatial Database for Automated Road Median Extraction

2018 ◽  
Author(s):  
Pankaj Kumar ◽  
Paul Lewis ◽  
Conor P. McElhinney
Keyword(s):  
Data Processing ◽  
Point Cloud ◽  
Laser Scanning ◽  
Data Retrieval ◽  
Lidar Data ◽  
Point Cloud Data ◽  
Cloud Data ◽  
The Road ◽  
Efficient Storage ◽  
Geospatial Database

Laser scanning systems make use of Light Detection and Ranging (LiDAR) technology to acquire accurately georeferenced sets of dense 3D point cloud data. The information acquired using these systems produces better knowledge about the terrain objects which are inherently 3D in nature. The LiDAR data acquired from mobile, airborne or terrestrial platforms provides several benefit over conventional sources of data acquisition in terms of accuracy, resolution and attributes. However, the large volume and scale of LiDAR data have inhibited the development of automated feature extraction algorithms due to the extensive computational cost involved in it. Moreover, the heterogeneously distributed point cloud, which represents objects with varying size, point density, holes and complicated structures pose a great challenge for data processing. Currently, geospatial database systems do not provide a robust solution for efficient storage and accessibility of raw data in a way that data processing could be applied based on optimal spatial extent. In this paper, we present Global LiDAR and Imagery Mobile Processing Spatial Environment (GLIMPSE) system that provides a framework for storage, management and integration of 3D LiDAR data acquired from multiple platforms. The system facilitates an efficient accessibility to the raw dataset, which is hierarchically represented in a geographically meaningful way. We utilise the GLIMPSE system to automatically extract road median from Airborne Laser Scanning (ALS) point cloud. In the first part of this paper, we detail an approach to efficiently retrieve the point cloud data from the GLIMPSE system for a particular geographic area based on user requirements. In the second part, we present an algorithm to automatically extract road median from the retrieved LiDAR data. The developed road median extraction algorithm utilises the LiDAR elevation and intensity attributes to distinguish the median from the road surface. We successfully tested our algorithms on two road sections consisting of distinct road median types based on concrete and grass-hedge barriers. The use of GLIMPSE improved the efficiency of the road median extraction in terms of fast accessibility to ALS point cloud data for the required road sections. The developed system and its associated algorithms provide a comprehensive solution to the user's requirement for an efficient storage, integration, retrieval and processing of large volumes of LiDAR point cloud data. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks.

scholarly journals UAV LiDAR Survey for Archaeological Documentation in Chiapas, Mexico

2021 ◽  
Vol 13 (23) ◽  
pp. 4731
Author(s):  
Whittaker Schroder ◽  
Timothy Murtha ◽  
Charles Golden ◽  
Andrew K. Scherer ◽  
Eben N. Broadbent ◽  
...  
Keyword(s):  
Land Use ◽  
High Precision ◽  
Landscape Change ◽  
Laser Scanning ◽  
Cultural Landscapes ◽  
Lidar Data ◽  
Airborne Laser ◽  
Extensive Documentation ◽  
Natural Landscapes

Airborne laser scanning has proven useful for rapid and extensive documentation of historic cultural landscapes after years of applications mapping natural landscapes and the built environment. The recent integration of unoccupied aerial vehicles (UAVs) with LiDAR systems is potentially transformative and offers complementary data for mapping targeted areas with high precision and systematic study of coupled natural and human systems. We report the results of data capture, analysis, and processing of UAV LiDAR data collected in the Maya Lowlands of Chiapas, Mexico in 2019 for a comparative landscape study. Six areas of archaeological settlement and long-term land-use reflecting a diversity of environments, land cover, and archaeological features were studied. These missions were characterized by areas that were variably forested, rugged, or flat, and included pre-Hispanic settlements and agrarian landscapes. Our study confirms that UAV LiDAR systems have great potential for broader application in high-precision archaeological mapping applications. We also conclude that these studies offer an important opportunity for multi-disciplinary collaboration. UAV LiDAR offers high-precision information that is not only useful for mapping archaeological features, but also provides critical information about long-term land use and landscape change in the context of archaeological resources.

Evaluation of the expediency of using thermal i magery data for decryption exogenous processes and vegetation

2018 ◽  
Vol 933 (3) ◽  
pp. 52-62
Author(s):  
V.S. Tikunov ◽  
I.A. Rylskiy ◽  
S.B. Lukatzkiy
Keyword(s):  
Spatial Data ◽  
Laser Scanning ◽  
Data Extraction ◽  
Spectral Band ◽  
Information Provision ◽  
Lidar Data ◽  
Thermal Imagery ◽  
Scanning Lidar ◽  
Field Works ◽  
Object Features

Innovative methods of aerial surveys changed approaches to information provision of projecting dramatically in last years. Nowadays there are several methods pretending to be the most efficient for collecting geospatial data intended for projecting – airborne laser scanning (LIDAR) data, RGB aerial imagery (forming 3D pointclouds) and orthoimages. Thermal imagery is one of the additional methods that can be used for projecting. LIDAR data is precise, it allows us to measure relief even under the vegetation, or to collect laser re-flections from wires, metal constructions and poles. Precision and completeness of the DEM, produced from LIDAR data, allows to define relief microforms. Airborne imagery (visual spectrum) is very widespread and can be easily depicted. Thermal images are more strange and less widespread, they use different way of image forming, and spectral features of ob-jects can vary in specific ways. Either way, the additional spectral band can be useful for achieving additional spatial data and different object features, it can minimize field works. Here different aspects of thermal imagery are described in comparison with RGB (visual) images, LIDAR data and GIS layers. The attempt to estimate the feasibility of thermal imag-es for new data extraction is made.

scholarly journals Linear-Based Incremental Co-Registration of MLS and Photogrammetric Point Clouds

2021 ◽  
Vol 13 (11) ◽  
pp. 2195
Author(s):  
Shiming Li ◽  
Xuming Ge ◽  
Shengfu Li ◽  
Bo Xu ◽  
Zhendong Wang
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Point Clouds ◽  
Registration Method ◽  
Cloud Data ◽  
Linear Feature ◽  
Data Registration ◽  
Source Point ◽  
Urban Remote Sensing ◽  
Single Data

Today, mobile laser scanning and oblique photogrammetry are two standard urban remote sensing acquisition methods, and the cross-source point-cloud data obtained using these methods have significant differences and complementarity. Accurate co-registration can make up for the limitations of a single data source, but many existing registration methods face critical challenges. Therefore, in this paper, we propose a systematic incremental registration method that can successfully register MLS and photogrammetric point clouds in the presence of a large number of missing data, large variations in point density, and scale differences. The robustness of this method is due to its elimination of noise in the extracted linear features and its 2D incremental registration strategy. There are three main contributions of our work: (1) the development of an end-to-end automatic cross-source point-cloud registration method; (2) a way to effectively extract the linear feature and restore the scale; and (3) an incremental registration strategy that simplifies the complex registration process. The experimental results show that this method can successfully achieve cross-source data registration, while other methods have difficulty obtaining satisfactory registration results efficiently. Moreover, this method can be extended to more point-cloud sources.

scholarly journals BIM Supported Surveying and Imaging Combination for Heritage Conservation

2021 ◽  
Vol 13 (8) ◽  
pp. 1584
Author(s):  
Pedro Martín-Lerones ◽  
David Olmedo ◽  
Ana López-Vidal ◽  
Jaime Gómez-García-Bermejo ◽  
Eduardo Zalama
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Cloud Data ◽  
Industry Foundation Classes ◽  
3D Point Clouds ◽  
Conservation Actions ◽  
Heritage Building ◽  
2D Imaging ◽  
Digital Survey ◽  
Level 2

As the basis for analysis and management of heritage assets, 3D laser scanning and photogrammetric 3D reconstruction have been probed as adequate techniques for point cloud data acquisition. The European Directive 2014/24/EU imposes BIM Level 2 for government centrally procured projects as a collaborative process of producing federated discipline-specific models. Although BIM software resources are intensified and increasingly growing, distinct specifications for heritage (H-BIM) are essential to driving particular processes and tools to efficiency shifting from point clouds to meaningful information ready to be exchanged using non-proprietary formats, such as Industry Foundation Classes (IFC). This paper details a procedure for processing enriched 3D point clouds into the REVIT software package due to its worldwide popularity and how closely it integrates with the BIM concept. The procedure will be additionally supported by a tailored plug-in to make high-quality 3D digital survey datasets usable together with 2D imaging, enhancing the capability to depict contextualized important graphical data to properly planning conservation actions. As a practical example, a 2D/3D enhanced combination is worked to accurately include into a BIM project, the length, orientation, and width of a big crack on the walls of the Castle of Torrelobatón (Spain) as a representative heritage building.

scholarly journals Monitoring of the Production Process of Graded Concrete Component Using Terrestrial Laser Scanning

2021 ◽  
Vol 13 (9) ◽  
pp. 1622
Author(s):  
Yihui Yang ◽  
Laura Balangé ◽  
Oliver Gericke ◽  
Daniel Schmeer ◽  
Li Zhang ◽  
...  
Keyword(s):  
Production Process ◽  
Laser Scanning ◽  
Hollow Spheres ◽  
Terrestrial Laser Scanning ◽  
Region Growing ◽  
Casting Process ◽  
Concrete Surface ◽  
Cloud Data ◽  
Sphere Detection ◽  
Concrete Component

Accepting the ecological necessity of a drastic reduction of resource consumption and greenhouse gas emissions in the building industry, the Institute for Lightweight Structures and Conceptual Design (ILEK) at the University of Stuttgart is developing graded concrete components with integrated concrete hollow spheres. These components weigh a fraction of usual conventional components while exhibiting the same performance. Throughout the production process of a component, the positions of the hollow spheres and the level of the fresh concrete have to be monitored with high accuracy and in close to real-time, so that the quality and structural performance of the component can be guaranteed. In this contribution, effective solutions of multiple sphere detection and concrete surface modeling based on the technology of terrestrial laser scanning (TLS) during the casting process are proposed and realized by the Institute of Engineering Geodesy (IIGS). A complete monitoring concept is presented to acquire the point cloud data fast and with high-quality. The data processing method for multiple sphere segmentation based on the efficient combination of region growing and random sample consensus (RANSAC) exhibits great performance on computational efficiency and robustness. The feasibility and reliability of the proposed methods are verified and evaluated by an experiment monitoring the production of an exemplary graded concrete component. Some suggestions to improve the monitoring performance and relevant future work are given as well.

scholarly journals An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

2021 ◽  
Vol 13 (15) ◽  
pp. 2938
Author(s):  
Feng Li ◽  
Haihong Zhu ◽  
Zhenwei Luo ◽  
Hang Shen ◽  
Lin Li
Keyword(s):  
Laser Scanning ◽  
Point Clouds ◽  
Airborne Laser Scanning ◽  
Superior Performance ◽  
Cloth Simulation ◽  
Cloud Data ◽  
Interpolation Filter ◽  
Landscape Characteristics ◽  
Surface Interpolation ◽  
Airborne Laser

Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

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