Classification of land cover and land use based on convolutional neural networks

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dc.identifier.uri http://dx.doi.org/10.15488/3436
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/3466
dc.contributor.author Yang, C.
dc.contributor.author Rottensteiner, Franz
dc.contributor.author Heipke, Christian
dc.date.accessioned 2018-06-08T11:57:14Z
dc.date.available 2018-06-08T11:57:14Z
dc.date.issued 2018
dc.identifier.citation Yang, C.; Rottensteiner, F.; Heipke, C.: Classification of land cover and land use based on convolutional neural networks. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4 (2018), Nr. 3, S. 251-258. DOI: https://doi.org/10.5194/isprs-annals-IV-3-251-2018
dc.description.abstract Land cover describes the physical material of the earth's surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7 % and 77.4 % can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification. © Authors 2018. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartofseries ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 4 (2018), Nr. 3
dc.rights CC BY 3.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/3.0/
dc.subject aerial imagery eng
dc.subject CNN eng
dc.subject geospatial land use database eng
dc.subject Land use classification eng
dc.subject semantic segmentation eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Classification of land cover and land use based on convolutional neural networks eng
dc.type article
dc.type Text
dc.relation.issn 2194-9042
dc.relation.doi https://doi.org/10.5194/isprs-annals-IV-3-251-2018
dc.bibliographicCitation.issue 3
dc.bibliographicCitation.volume 4
dc.bibliographicCitation.firstPage 251
dc.bibliographicCitation.lastPage 258
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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