dc.identifier.uri | http://dx.doi.org/10.15488/5042 | |
dc.identifier.uri | https://www.repo.uni-hannover.de/handle/123456789/5086 | |
dc.contributor.author | Rottensteiner, Franz | |
dc.contributor.author | Sohn, Gunho | |
dc.contributor.author | Jung, Jaewook | |
dc.contributor.author | Gerke, Markus | |
dc.contributor.author | Baillard, Caroline | |
dc.contributor.author | Benitez, Sebastien | |
dc.contributor.author | Breitkopf, Uwe | |
dc.contributor.editor | Shortis, M. | |
dc.contributor.editor | Paparoditis, N. | |
dc.contributor.editor | Mallet C. | |
dc.date.accessioned | 2019-06-27T07:47:38Z | |
dc.date.available | 2019-06-27T07:47:38Z | |
dc.date.issued | 2012 | |
dc.identifier.citation | Rottensteiner, Franz; Sohn, Gunho; Jung, Jaewook; Gerke, Markus; Baillard, Caroline et al.: The ISPRS benchmark on urban object classification and 3d building reconstruction. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences I-3 (2012), Nr. 1, S. 293-298. DOI: https://doi.org/10.5194/isprsannals-i-3-293-2012 | |
dc.description.abstract | For more than two decades, many efforts have been made to develop methods for extracting urban objects from data acquired by airborne sensors. In order to make the results of such algorithms more comparable, benchmarking data sets are of paramount importance. Such a data set, consisting of airborne image and laserscanner data, has been made available to the scientific community. Researchers were encouraged to submit results of urban object detection and 3D building reconstruction, which were evaluated based on reference data. This paper presents the outcomes of the evaluation for building detection, tree detection, and 3D building reconstruction. The results achieved by different methods are compared and analysed to identify promising strategies for automatic urban object extraction from current airborne sensor data, but also common problems of state-of-the-art methods. | eng |
dc.language.iso | eng | |
dc.publisher | Göttingen : Copernicus GmbH | |
dc.relation.ispartof | XXII ISPRS Congress, Technical Commission III | |
dc.relation.ispartofseries | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; I-3 | |
dc.rights | CC BY 3.0 Unported | |
dc.rights.uri | https://creativecommons.org/licenses/by/3.0/ | |
dc.subject | Data mining | eng |
dc.subject | Artificial intelligence | eng |
dc.subject | Object detection | eng |
dc.subject | Reference data (financial markets) | eng |
dc.subject | Computer vision | eng |
dc.subject | Computer science | eng |
dc.subject | Data set | eng |
dc.subject | Remote sensing | eng |
dc.subject | Benchmarking | eng |
dc.subject.classification | Konferenzschrift | ger |
dc.subject.ddc | 550 | Geowissenschaften | ger |
dc.title | The ISPRS benchmark on urban object classification and 3d building reconstruction | eng |
dc.type | Article | |
dc.type | Text | |
dc.relation.essn | 2194-9050 | |
dc.relation.issn | 2194-9050 | |
dc.relation.doi | https://doi.org/10.5194/isprsannals-i-3-293-2012 | |
dc.bibliographicCitation.issue | 1 | |
dc.bibliographicCitation.volume | I-3 | |
dc.bibliographicCitation.firstPage | 293 | |
dc.bibliographicCitation.lastPage | 298 | |
dc.description.version | publishedVersion | |
tib.accessRights | frei zug�nglich |
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