A 6-DIMENSIONAL HILBERT APPROACH TO INDEX FULL WAVEFORM LiDAR DATA IN A DISTRIBUTED COMPUTING ENVIRONMENT

Laser scanning data are increasingly available across the globe. To maximize the data's usability requires proper storage and indexing. While significant research has been invested in developing storage and indexing solutions for laser scanning point clouds (i.e. using the discrete form of the data), little attention has been paid to developing equivalent solutions for full waveform (FWF) laser scanning data, especially in a distributed computing environment. Given the growing availability of FWF sensors and datasets, FWF data management solutions are increasingly needed. This paper presents an attempt towards establishing a scalable solution for handling large FWF datasets by introducing the distributed computing solution for FWF data. The work involves a FWF database built atop HBase – the distributed database system running on Hadoop commodity clusters. By combining a 6-dimensional (6D) Hilbert spatial code and a temporal index into a compound indexing key, the database system is capable of supporting multiple spatial, temporal, and spatio-temporal queries. Such queries are important for FWF data exploration and dissemination. The proposed spatial decomposition at a fine resolution of 0.05 m allows the storage of each LiDAR FWF measurement (i.e. pulse, waves, and points) on a single row of the database, thereby providing the full capabilities to add, modify, and remove each measurement record anatomically. While the feasibility and capabilities of the 6D Hilbert solution are evident, the Hilbert decomposition is not due to the complications from the combination of the data’s high dimensionality, fine resolution, and large spatial extent. These factors lead to a complex set of both attractive attributes and limitation in the proposed solution, which are described in this paper based on experimental tests using a 1.1 billion pulse LiDAR scan of a portion of Dublin, Ireland

Development of a Precise Tree Structure from LiDAR Point Clouds

A precise tree structure that represents the distribution of tree stem, branches, and leaves is crucial for accurately capturing the full representation of a tree. Light Detection and Ranging (LiDAR)-based three-dimensional (3D) point clouds (PCs) capture the geometry of scanned objects including forests stands and individual trees. PCs are irregular, unstructured, often noisy, and contaminated by outliers. Researchers have struggled to develop methods to separate leaves and wood without losing the tree geometry. This paper proposes a solution that employs only the spatial coordinates (x, y, z) of the PC. The new algorithm works as a filtering approach, utilizing multi-scale neighborhood-based geometric features (GFs) e.g., linearity, planarity, and verticality to classify linear (wood) and non-linear (leaf) points. This involves finding potential wood points and coupling them with an octree-based segmentation to develop a tree architecture. The main contributions of this paper are (i) investigating the potential of different GFs to split linear and non-linear points, (ii) introducing a novel method that pointwise classifies leaf and wood points, and (iii) developing a precise 3D tree structure. The performance of the new algorithm has been demonstrated through terrestrial laser scanning PCs. For a Scots pine tree, the new method classifies leaf and wood points with an overall accuracy of 97.9%

Characteristic damage concentration: a new damage index for historic interiors

Eighth International Conference on Structural Analysis of Historical ConstructionsA crack-by-crack documentation and characterization of damage for historic interiors is often an extremely resource-intensive process resulting in either the investment of an enormous amount of resources or a low-quality record. Neither situation readily lends itself to longitudinal studies. To overcome this situation, a new damage documentation system is devised for the recording and categorization of damage for the interior spaces of historic structures. In this paper, the system is presented in the context of an early 20th century American church that was experiencing on-going damage. The structure was evaluated three times over a six-year period. The graphical results enable visualization of the progression of damage over time and an acute understanding as to its spatial distribution.Deposited by bulk impor

A Ritz's method based solution for the contact problem of a deformable rectangular plate on an elastic quarter-space

In this article, Ritz’s method is used to calculate with unprecedented accuracy the displacements related to a deformable rectangular plate resting on the surface of an elastic quarter-space. To achieve this required three basic steps. The first step involved the study of Green’s function describing the vertical displacements of the surface of an elastic quarter-space due to vertical force applied on its surface. For this case, an explicit formula was obtained by analytically resolving a complicated integral that did not previously have an analytical solution. The second step involved the study of the coupled system of a plate and an elastic quarter-space. This portion focused on determining reactive forces in the contact zone based on Hetenyi’s solution. After determination of the reactive forces, certain features were attributed to the plate’s edges. The final step involved the application of Ritz’s method to determine the deflections of the plate resting on the surface of the quarter-space. Finally, an example calculation and validation of results are given. This is the first semi-analytical solution proposed for this type of contact problem

Automatic detection of road edges from aerial laser scanning data

When aerial laser scanning (ALS) is deployed with targeted flight path planning, urban scenes can be captured in points clouds with both high vertical and horizontal densities to support a new generation of urban analysis and applications. As an example, this paper proposes a hierarchical method to automatically extract data points describing road edges, which are then used for reconstructing road edges and identifying accessible passage areas. The proposed approach is a cell-based method consisting of 3 main steps: (1) filtering rough ground points, (2) extracting cells containing data points of the road curb, and (3) eliminating incorrect road curb segments. The method was tested on a pair of 100 m &amp;times; 100 m tiles of ALS data of Dublin Ireland's city center with a horizontal point density of about 325 points/m2. Results showed the data points of the road edges to be extracted properly for locations appearing as the road edges with the average distance errors of 0.07 m and the ratio between the extracted road edges and the ground truth by 73.2%.</p

A new multi-parallel condition assessment scale for estimating tunnel-induced damage

Development of Urban Areas and Geotechnical Engineering. June 16-19, 2008, St. Petersburg, RussiaTraditional means to predict tunnel-induced damage for large groups of potentially affected above ground structures has generally focused on creating a settlement trough and estimating subsequent building response limited by rudimentary aspects of each building’s geometry, structural system, foundation type, and soil bearing capacity. Historically, the procedure is done without consideration for a building’s condition. In this paper, a supplementary step is proposed – one focused on the building’s current state of repair. The presented system employs at its core a widely adopted crack evaluation scale. The main focus is application to unreinforced masonry buildings as they are simultaneously, disproportionately present in urban areas and vulnerable to tunnel movements. The proposed system is herein outlined and applied to a future tunnelling project in Ireland.Science Foundation Irelan

A new multi-parallel condition assessment scale for estimating tunnel-induced damage

Development of Urban Areas and Geotechnical Engineering. June 16-19, 2008, St. Petersburg, RussiaTraditional means to predict tunnel-induced damage for large groups of potentially affected above ground structures has generally focused on creating a settlement trough and estimating subsequent building response limited by rudimentary aspects of each building’s geometry, structural system, foundation type, and soil bearing capacity. Historically, the procedure is done without consideration for a building’s condition. In this paper, a supplementary step is proposed – one focused on the building’s current state of repair. The presented system employs at its core a widely adopted crack evaluation scale. The main focus is application to unreinforced masonry buildings as they are simultaneously, disproportionately present in urban areas and vulnerable to tunnel movements. The proposed system is herein outlined and applied to a future tunnelling project in Ireland.Science Foundation Irelan

Automatic detection of road edges from aerial laser scanning data

When aerial laser scanning (ALS) is deployed with targeted flight path planning, urban scenes can be captured in points clouds with both high vertical and horizontal densities to support a new generation of urban analysis and applications. As an example, this paper proposes a hierarchical method to automatically extract data points describing road edges, which are then used for reconstructing road edges and identifying accessible passage areas. The proposed approach is a cell-based method consisting of 3 main steps: (1) filtering rough ground points, (2) extracting cells containing data points of the road curb, and (3) eliminating incorrect road curb segments. The method was tested on a pair of 100 m &amp;times; 100 m tiles of ALS data of Dublin Ireland's city center with a horizontal point density of about 325 points/m2. Results showed the data points of the road edges to be extracted properly for locations appearing as the road edges with the average distance errors of 0.07 m and the ratio between the extracted road edges and the ground truth by 73.2%.Optical and Laser Remote Sensin

A Two-Step Feature Extraction Algorithm: Application to deep learning for point cloud classification

Most deep learning (DL) methods that are not end-to-end use several multi-scale and multi-type hand-crafted features that make the network challenging, more computationally intensive and vulnerable to overfitting. Furthermore, reliance on empirically-based feature dimensionality reduction may lead to misclassification. In contrast, efficient feature management can reduce storage and computational complexities, builds better classifiers, and improves overall performance. Principal Component Analysis (PCA) is a well-known dimension reduction technique that has been used for feature extraction. This paper presents a two-step PCA based feature extraction algorithm that employs a variant of feature-based PointNet (Qi et al., 2017a) for point cloud classification. This paper extends the PointNet framework for use on large-scale aerial LiDAR data, and contributes by (i) developing a new feature extraction algorithm, (ii) exploring the impact of dimensionality reduction in feature extraction, and (iii) introducing a non-end-to-end PointNet variant for per point classification in point clouds. This is demonstrated on aerial laser scanning (ALS) point clouds. The algorithm successfully reduces the dimension of the feature space without sacrificing performance, as benchmarked against the original PointNet algorithm. When tested on the well-known Vaihingen data set, the proposed algorithm achieves an Overall Accuracy (OA) of 74.64% by using 9 input vectors and 14 shape features, whereas with the same 9 input vectors and only 5PCs (principal components built by the 14 shape features) it actually achieves a higher OA of 75.36% which demonstrates the effect of efficient dimensionality reduction. Optical and Laser Remote Sensin

A Systematic Approach for Large-scale, Rapid, Dilapidation Surveys of Historic, Masonry Buildings

Dilapidation surveys may require extensive resources to achieve detailed accounts of damage for intervention purposes or may involve only limited resources but be restricted to an extremely rapid assessment (e.g. post-earthquake, life-safety inspection). Neither provides a holistic, cost-effective approach for evaluating the general health of a large number of structures, as is needed for urban planning, historic designation determination, and risk assessment due to adjacent works. To overcome this limitation, index images are introduced for a systematic approach for rapidly conducting large-scale, dilapidation surveys of historic masonry buildings. This method, the University College Dublin Inspection Method (UCDIM), is tested against both a detailed inspection and an alternative rapid approach to determine accuracy and resource intensiveness through its application by three inspectors of various levels of experience to six buildings in the city centre of Dublin, Ireland. The UCDIM provided a damage ranking of ρ = 0.94 for all inspectors, regardless of experience, except when painted or rendered façades were included. The UCDIM, when compared to detailed inspection provided a high level of reliability, cost savings of approximately 90% and several months of time savings since interior access was not required.Deposited by bulk importSB. 19/02/2013Item embargoed until one year after publication but publication date unknown - DG 30/08/201