EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
In this paper, we present a patch-based land use and land cover classification approach using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible, and are provided in the earth observation program Copernicus. We present a novel dataset, based on these images...
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| Veröffentlicht in: | IEEE journal of selected topics in applied earth observations and remote sensing Jg. 12; H. 7; S. 2217 - 2226 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Piscataway
IEEE
01.07.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 1939-1404, 2151-1535 |
| Online-Zugang: | Volltext |
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| Abstract | In this paper, we present a patch-based land use and land cover classification approach using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible, and are provided in the earth observation program Copernicus. We present a novel dataset, based on these images that covers 13 spectral bands and is comprised of ten classes with a total of 27 000 labeled and geo-referenced images. Benchmarks are provided for this novel dataset with its spectral bands using state-of-the-art deep convolutional neural networks. An overall classification accuracy of 98.57% was achieved with the proposed novel dataset. The resulting classification system opens a gate toward a number of earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes, and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available at https://github.com/phelber/eurosat. |
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| AbstractList | In this paper, we present a patch-based land use and land cover classification approach using Sentinel-2 satellite images. The Sentinel-2 satellite images are openly and freely accessible, and are provided in the earth observation program Copernicus. We present a novel dataset, based on these images that covers 13 spectral bands and is comprised of ten classes with a total of 27 000 labeled and geo-referenced images. Benchmarks are provided for this novel dataset with its spectral bands using state-of-the-art deep convolutional neural networks. An overall classification accuracy of 98.57% was achieved with the proposed novel dataset. The resulting classification system opens a gate toward a number of earth observation applications. We demonstrate how this classification system can be used for detecting land use and land cover changes, and how it can assist in improving geographical maps. The geo-referenced dataset EuroSAT is made publicly available at https://github.com/phelber/eurosat . |
| Author | Helber, Patrick Dengel, Andreas Borth, Damian Bischke, Benjamin |
| Author_xml | – sequence: 1 givenname: Patrick orcidid: 0000-0001-8454-4301 surname: Helber fullname: Helber, Patrick email: Patrick.Helber@dfki.de organization: Technische Universität Kaiserslautern, Kaiserslautern, Germany – sequence: 2 givenname: Benjamin orcidid: 0000-0002-6473-3348 surname: Bischke fullname: Bischke, Benjamin email: Benjamin.Bischke@dfki.de organization: Technische Universität Kaiserslautern, Kaiserslautern, Germany – sequence: 3 givenname: Andreas surname: Dengel fullname: Dengel, Andreas email: Andreas.Dengel@dfki.de organization: Technische Universität Kaiserslautern, Kaiserslautern, Germany – sequence: 4 givenname: Damian surname: Borth fullname: Borth, Damian email: damian.borth@unisg.ch organization: Institute for Computer Science, University of St. Gallen, St. Gallen, Switzerland |
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| Snippet | In this paper, we present a patch-based land use and land cover classification approach using Sentinel-2 satellite images. The Sentinel-2 satellite images are... |
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| SubjectTerms | Artificial neural networks Band spectra Benchmark testing Benchmarks Classification Dataset Datasets deep convolutional neural network Deep learning Earth earth observation Feature extraction Image classification Land cover land cover classification Land use land use classification Machine learning Neural networks Remote sensing satellite image classification Satellite imagery satellite images Satellites Spaceborne remote sensing Spatial resolution Spectral bands |
| Title | EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification |
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| Volume | 12 |
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