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

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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

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/3436

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Sum total of downloads: 771




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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 As such databases become outdated quickly, an automatic update process is required.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.
License of this version: CC BY 3.0 Unported
Document Type: article
Publishing status: publishedVersion
Issue Date: 2018
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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pos. country downloads
total perc.
1 image of flag of Germany Germany 179 23.22%
2 image of flag of China China 99 12.84%
3 image of flag of United States United States 69 8.95%
4 image of flag of Korea, Republic of Korea, Republic of 50 6.49%
5 image of flag of India India 48 6.23%
6 image of flag of Tunisia Tunisia 32 4.15%
7 image of flag of Hong Kong Hong Kong 25 3.24%
8 image of flag of United Kingdom United Kingdom 24 3.11%
9 image of flag of Turkey Turkey 15 1.95%
10 image of flag of No geo information available No geo information available 14 1.82%
    other countries 216 28.02%

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