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dc.contributor.authorAwrangjeb, M
dc.contributor.authorLu, G
dc.contributor.editorAbdesselam Bouzerdoum, Lei Wang, Philip Ogunbona, Wanqing Li, Son Lam Phung
dc.date.accessioned2018-01-08T01:39:46Z
dc.date.available2018-01-08T01:39:46Z
dc.date.issued2015
dc.identifier.isbn9781479954094
dc.identifier.doi10.1109/DICTA.2014.7008096
dc.identifier.urihttp://hdl.handle.net/10072/99594
dc.description.abstractThis paper presents a segmentation of LIDAR point cloud data for automatic extraction of building footprint. Using the ground height information from a DEM (Digital Elevation Model), the non-ground points (mainly buildings and trees) are separated from the ground points. Points on walls are removed from the set of non-ground points. The remaining non-ground points are then divided into clusters based on height and local neighbourhood. Planar roof segments are extracted from each cluster of points following a region-growing technique. Planes are initialised using coplanar points as seed points and then grown using plane compatibility tests. Once all the planar segments are extracted, a rule-based procedure is applied to remove tree planes which are small in size and randomly oriented. The neighbouring planes are then merged to obtain individual building boundaries, which are regularised based on a new feature-based technique. Corners and line-segments are extracted from each boundary and adjusted using the assumption that each short building side is parallel or perpendicular to one or more neighbouring long building sides. Experimental results on five Australian data sets show that the proposed method offers higher correctness rate in building footprint extraction than a state-of-the-art method.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.publisher.placeUnited States
dc.relation.ispartofconferencenameDICTA 2014
dc.relation.ispartofconferencetitle2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014
dc.relation.ispartofdatefrom2014-11-25
dc.relation.ispartofdateto2014-11-27
dc.relation.ispartoflocationWollongong, Australia
dc.subject.fieldofresearchImage Processing
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchPhotogrammetry and Remote Sensing
dc.subject.fieldofresearchcode080106
dc.subject.fieldofresearchcode080104
dc.subject.fieldofresearchcode090905
dc.titleAutomatic Building Footprint Extraction and Regularisation from LIDAR Point Cloud Data
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorAwrangjeb, Mohammad


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    Contains papers delivered by Griffith authors at national and international conferences.

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