Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/19555
Title: A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation
Authors: Miltiadou, Milto 
Campbell, Neill D.F. 
Cosker, Darren 
Grant, Michael G. 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Volumetric data;Execution time;LiDAR;Voxelisation;Iso-surface;Visualisations;Data structures;Efficiency;Memory management
Issue Date: 4-Feb-2021
Source: Remote Sensing, vol. 13, iss. 4, 2021
Volume: 13
Issue: 4
Project: ERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environment 
Related Dataset(s): http://data.ceda.ac.uk/neodc/arsf/2010/FW10_01/FW10_01-2010_098_New_Forest/LiDAR/fw_laser
http://data.ceda.ac.uk/neodc/arsf/2010/FW10_01/FW10_01-2010_187_Dennys_wood/LiDAR/fw_laser
http://data.ceda.ac.uk/neodc/arsf/2014/GB12_04/GB12_04-2014_083_Eaves_Wood/LiDAR/flightlines/fw_laser/las1.3
Journal: Remote Sensing 
Abstract: In this paper, we investigate the performance of six data structures for managing {voxelised} full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.
URI: https://ktisis.cut.ac.cy/handle/10488/19555
ISSN: 2072-4292
DOI: 10.3390/rs13040559
Rights: © by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Type: Article
Affiliation : Plymouth Marine Laboratory 
Cyprus University of Technology 
Appears in Collections:Publications under the auspices of the EXCELSIOR H2020 Teaming Project/ERATOSTHENES Centre of Excellence

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