Data mining for classification of high volume dense lidar data in an urban area

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dc.identifier.uri http://dx.doi.org/10.15488/4989
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/5033
dc.contributor.author Chauhan, I.
dc.contributor.author Brenner, Claus
dc.date.accessioned 2019-06-26T06:32:25Z
dc.date.available 2019-06-26T06:32:25Z
dc.date.issued 2018
dc.identifier.citation Chauhan, I.; Brenner, Claus: Data mining for classification of high volume dense lidar data in an urban area. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5 (2018), S. 391-395. DOI: https://doi.org/10.5194/isprs-annals-iv-5-391-2018
dc.description.abstract 3D LiDAR point cloud obtained from the laser scanner is too dense and contains millions of points with information. For such huge volume of data to be sorted, identified, validated and be used for prediction, data mining provides immense scope and has been used to achieve the same. Certain unique attributes were selected as an input for creating models through machine learning. Supervised models were thus built for prediction of classes through the available LiDAR data using random forest algorithm. The algorithm was chosen owing to its efficiency and accuracy over other data mining algorithms. The models created using random forest were then tested on an unclassified point cloud data of an urban area. The method shows promising results in terms of classification accuracy as overall accuracy of 91.71 % was achieved for pixel-based classification. The method also displays enhanced efficiency over common classification algorithms as the time taken to make predictions about the data is reduced considerably for a set of dense LiDAR data. This shows positive foresight of making use of data mining and machine learning to handle large volume of LiDAR data and can go a long way in augmenting efficient processing of LiDAR data. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartofseries ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-5 (2018)
dc.rights CC BY 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Statistical classification eng
dc.subject Data mining eng
dc.subject Point cloud eng
dc.subject Artificial intelligence eng
dc.subject Pixel eng
dc.subject Random forest eng
dc.subject Computer vision eng
dc.subject Computer science eng
dc.subject Lidar eng
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Data mining for classification of high volume dense lidar data in an urban area eng
dc.type article
dc.type Text
dc.relation.issn 2194-9050
dc.relation.doi https://doi.org/10.5194/isprs-annals-iv-5-391-2018
dc.bibliographicCitation.volume IV-5
dc.bibliographicCitation.firstPage 391
dc.bibliographicCitation.lastPage 395
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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