Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/19555
DC FieldValueLanguage
dc.contributor.authorMiltiadou, Milto-
dc.contributor.authorCampbell, Neill D.F.-
dc.contributor.authorCosker, Darren-
dc.contributor.authorGrant, Michael G.-
dc.date.accessioned2021-02-09T09:40:55Z-
dc.date.available2021-02-09T09:40:55Z-
dc.date.issued2021-02-04-
dc.identifier.citationRemote Sensing, 2021, vol. 13, no. 4, articl. no. 559en_US
dc.identifier.issn2072-4292-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/19555-
dc.description.abstractIn 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.en_US
dc.description.sponsorshipThe Centre for Digital Entertainment, United Kingdom Plymouth Marine Laboratory, United Kingdomen_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relationERATOSTHENES: Excellence Research Centre for Earth Surveillance and Space-Based Monitoring of the Environmenten_US
dc.relation.ispartofRemote Sensingen_US
dc.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/).en_US
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectVolumetric dataen_US
dc.subjectExecution timeen_US
dc.subjectLiDARen_US
dc.subjectVoxelisationen_US
dc.subjectIso-surfaceen_US
dc.subjectVisualisationsen_US
dc.subjectData structuresen_US
dc.subjectEfficiencyen_US
dc.subjectMemory managementen_US
dc.titleA Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creationen_US
dc.typeArticleen_US
dc.collaborationPlymouth Marine Laboratoryen_US
dc.collaborationCyprus University of Technologyen_US
dc.subject.categoryCivil Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryUnited Kingdomen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.relation.datasethttp://data.ceda.ac.uk/neodc/arsf/2010/FW10_01/FW10_01-2010_098_New_Forest/LiDAR/fw_laseren_US
dc.relation.datasethttp://data.ceda.ac.uk/neodc/arsf/2010/FW10_01/FW10_01-2010_187_Dennys_wood/LiDAR/fw_laseren_US
dc.relation.datasethttp://data.ceda.ac.uk/neodc/arsf/2014/GB12_04/GB12_04-2014_083_Eaves_Wood/LiDAR/flightlines/fw_laser/las1.3en_US
dc.identifier.doi10.3390/rs13040559en_US
dc.relation.issue4en_US
dc.relation.volume13en_US
cut.common.academicyear2020-2021en_US
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.grantfulltextopen-
item.openairetypearticle-
item.cerifentitytypePublications-
crisitem.author.deptDepartment of Civil Engineering and Geomatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4715-5048-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.project.funderEuropean Commission-
crisitem.project.grantnoH2020-WIDESPREAD-2018-01 / WIDESPREAD-01-2018-2019 Teaming Phase 2-
crisitem.project.fundingProgramH2020 Spreading Excellence, Widening Participation, Science with and for Society-
crisitem.project.openAireinfo:eu-repo/grantAgreeent/EC/H2020/857510-
crisitem.journal.journalissn2072-4292-
crisitem.journal.publisherMDPI-
Appears in Collections:Publications under the auspices of the EXCELSIOR H2020 Teaming Project/ERATOSTHENES Centre of Excellence
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