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dc.contributor.authorAwrangjeb, Mohammad
dc.date.accessioned2018-01-05T03:00:46Z
dc.date.available2018-01-05T03:00:46Z
dc.date.issued2015
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs71014119
dc.identifier.urihttp://hdl.handle.net/10072/172463
dc.description.abstractPeriodic building change detection is important for many applications, including disaster management. Building map databases need to be updated based on detected changes so as to ensure their currency and usefulness. This paper first presents a graphical user interface (GUI) developed to support the creation of a building database from building footprints automatically extracted from LiDAR (light detection and ranging) point cloud data. An automatic building change detection technique by which buildings are automatically extracted from newly-available LiDAR point cloud data and compared to those within an existing building database is then presented. Buildings identified as totally new or demolished are directly added to the change detection output. However, for part-building demolition or extension, a connected component analysis algorithm is applied, and for each connected building component, the area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building-part. Using the developed GUI, a user can quickly examine each suggested change and indicate his/her decision to update the database, with a minimum number of mouse clicks. In experimental tests, the proposed change detection technique was found to produce almost no omission errors, and when compared to the number of reference building corners, it reduced the human interaction to 14% for initial building map generation and to 3% for map updating. Thus, the proposed approach can be exploited for enhanced automated building information updating within a topographic database.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherMDPI AG
dc.relation.ispartofpagefrom14119
dc.relation.ispartofpageto14150
dc.relation.ispartofissue10
dc.relation.ispartofjournalRemote Sensing
dc.relation.ispartofvolume7
dc.subject.fieldofresearchImage Processing
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchPhotogrammetry and Remote Sensing
dc.subject.fieldofresearchClassical Physics
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode080106
dc.subject.fieldofresearchcode080104
dc.subject.fieldofresearchcode090905
dc.subject.fieldofresearchcode0203
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode0909
dc.titleEffective generation and update of a building map database through automatic building change detection from LiDAR point cloud data
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionVersion of Record (VoR)
gro.rights.copyright© 2015 by the author; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorAwrangjeb, Mohammad


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