Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89311
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorMegahed, Yen_US
dc.creatorShaker, Aen_US
dc.creatorYan, WYen_US
dc.date.accessioned2021-03-10T05:58:51Z-
dc.date.available2021-03-10T05:58:51Z-
dc.identifier.urihttp://hdl.handle.net/10397/89311-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rightsCopyright: © 2021 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.rightsThe following publication Megahed, Y.; Shaker, A.; Yan, W.Y. Fusion of Airborne LiDAR Point Clouds and Aerial Images for Heterogeneous Land-Use Urban Mapping. Remote Sens. 2021, 13, 814 is available at https://doi.org/10.3390/rs13040814.en_US
dc.subjectUrban land-useen_US
dc.subjectLiDAR-aerial integrationen_US
dc.subjectLiDAR-aerial geo-registrationen_US
dc.subjectLiDAR classificationen_US
dc.subjectSupervised machine learningen_US
dc.subjectMaximum likelihooden_US
dc.subjectSupport vector machinesen_US
dc.subjectNeural networksen_US
dc.subjectBootstrap aggregationen_US
dc.subjectk-fold cross-validationen_US
dc.titleFusion of airborne LiDAR point clouds and aerial images for heterogeneous land-use urban mappingen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1en_US
dc.identifier.epage36en_US
dc.identifier.volume13en_US
dc.identifier.issue4en_US
dc.identifier.doi10.3390/rs13040814en_US
dcterms.abstractThe World Health Organization has reported that the number of worldwide urban residents is expected to reach 70% of the total world population by 2050. In the face of challenges brought about by the demographic transition, there is an urgent need to improve the accuracy of urban land-use mappings to more efficiently inform about urban planning processes. Decision-makers rely on accurate urban mappings to properly assess current plans and to develop new ones. This study investigates the effects of including conventional spectral signatures acquired by different sensors on the classification of airborne LiDAR (Light Detection and Ranging) point clouds using multiple feature spaces. The proposed method applied three machine learning algorithms—ML (Maximum Likelihood), SVM (Support Vector Machines), and MLP (Multilayer Perceptron Neural Network)—to classify LiDAR point clouds of a residential urban area after being geo-registered to aerial photos. The overall classification accuracy passed 97%, with height as the only geometric feature in the classifying space. Misclassifications occurred among different classes due to independent acquisition of aerial and LiDAR data as well as shadow and orthorectification problems from aerial images. Nevertheless, the outcomes are promising as they surpassed those achieved with large geometric feature spaces and are encouraging since the approach is computationally reasonable and integrates radiometric properties from affordable sensors.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationRemote sensing, 2 Feb. 2021, v. 13, no. 4, 814, p. 1-36en_US
dcterms.isPartOfRemote sensingen_US
dcterms.issued2021-02-02-
dc.identifier.eissn2072-4292en_US
dc.identifier.artn814en_US
dc.description.validate202103 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumbera0604-n02-
dc.identifier.SubFormID562-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingText25213320||P0030506en_US
dc.description.pubStatusPublisheden_US
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