Learning cartographic building generalization with deep convolutional neural networks

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dc.identifier.uri http://dx.doi.org/10.15488/8808
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/8861
dc.contributor.author Feng, Yu
dc.contributor.author Thiemann, Frank
dc.contributor.author Sester, Monika
dc.date.accessioned 2019-12-11T16:44:50Z
dc.date.available 2019-12-11T16:44:50Z
dc.date.issued 2019
dc.identifier.citation Feng, Y.; Thiemann, F.; Sester, M.: Learning cartographic building generalization with deep convolutional neural networks. In: ISPRS International Journal of Geo-Information 8 (2019), Nr. 6, 258. DOI: https://doi.org/10.3390/ijgi8060258
dc.description.abstract Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g., simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the human operator is the benchmark, who is able to design an aesthetic and correct representation of the physical reality. Deep learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform traditional computer vision methods. In both domains-computer vision and cartography-humans are able to produce good solutions. A prerequisite for the application of deep learning is the availability of many representative training examples for the situation to be learned. As this is given in cartography (there are many existing map series), the idea in this paper is to employ deep convolutional neural networks (DCNNs) for cartographic generalizations tasks, especially for the task of building generalization. Three network architectures, namely U-net, residual U-net and generative adversarial network (GAN), are evaluated both quantitatively and qualitatively in this paper. They are compared based on their performance on this task at target map scales 1:10,000, 1:15,000 and 1:25,000, respectively. The results indicate that deep learning models can successfully learn cartographic generalization operations in one single model in an implicit way. The residual U-net outperforms the others and achieved the best generalization performance. eng
dc.language.iso eng
dc.publisher Basel : MDPI AG
dc.relation.ispartofseries ISPRS International Journal of Geo-Information 8 (2019), Nr. 6
dc.rights CC BY 4.0 Unported
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Cartography eng
dc.subject Deep convolutional neural networks eng
dc.subject Geometry simplification eng
dc.subject Map generalization eng
dc.subject.ddc 004 | Informatik ger
dc.title Learning cartographic building generalization with deep convolutional neural networks
dc.type article
dc.type Text
dc.relation.issn 22209964
dc.relation.doi https://doi.org/10.3390/ijgi8060258
dc.bibliographicCitation.volume 8
dc.bibliographicCitation.firstPage 258
dc.description.version publishedVersion
tib.accessRights frei zug�nglich

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