Cited by 1,951Remote-Sensing Image ClassificationRemote Sensing and Land UseRemote Sensing in Agriculture

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EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

Helber, Patrick, Bischke, Benjamin, Dengel, Andreas, Borth, Damian·2019·IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI 10.1109/jstars.2019.2918242

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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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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
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LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
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Show all 39 references

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Authors: Helber, P., Bischke, B., Dengel, A., & Borth, D.
Affiliations: University of Kaiserslautern (DE)
University of St.Gallen (CH)
ORCID: Helber, Patrick
Bischke, Benjamin
Dengel, Andreas
Borth, Damian
Venue: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019-07
Volume: 12
Issue: 7
Pages: 2217-2226
ISSN: 1939-1404 (ISSN-L)
2151-1535
DOI: 10.1109/jstars.2019.2918242
OpenAlex: W2964194231
Cited by: 1,951
References: 39
Citations by year: 2026: 430 · 2025: 505 · 2024: 352 · 2023: 257 · 2022: 167 · 2021: 140 · 2020: 77 · 2019: 19
FWCI: 67.55
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion)
Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
Funders: BMBF Project DeFuseNN (01IW17002)
Nvidia (xxx)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
Social Sciences › Geography, Planning and Development › Remote Sensing and Land Use
Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
Keywords: land use and land cover classification, EuroSAT dataset, Sentinel-2, satellite imagery, deep learning benchmark, convolutional neural networks, patch-based classification, geo-referenced images, Copernicus

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EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

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