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Dataset . 2018
Data sources: Datacite
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ZENODO
Dataset . 2018
Data sources: Datacite
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ZENODO
Dataset . 2018
Data sources: Datacite
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EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification

Authors: Helber, Patrick; Bischke, Benjamin; Dengel, Andreas; Borth, Damian;

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

Abstract

EuroSAT is a land use and land cover classification dataset. The dataset is based on Sentinel-2 satellite imagery covering 13 spectral bands and consists of 10 LULC classes with a total of 27,000 labeled and geo-referenced images. The dataset is associated with the publications "Introducing EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification" and "EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification". EuroSAT_RGB.zip contains the RGB version of the dataset, which includes the optical R, G and B frequency bands encoded as JPEG images. EuroSAT_MS.zip contains the multi-spectral version of the EuroSAT dataset, which includes all 13 Sentinel-2 bands in the original value range.

Sentinel data is free and open to the public under EU law. Please consider the Copernicus Sentinel Data Terms and Conditions (https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice) when using Copernicus Sentinel data. The work associated with the creation of the dataset and the publication of the related article and conference paper was partially funded by the BMBF project DeFuseNN (01IW17002). The authors thank NVIDIA for the support within the NVIDIA AI Lab program.

Keywords

remote sensing, land cover classification, land use classification, machine learning, deep convolutional neural network, sentinel-2, deep learning, earth observation, satellite images

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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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