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image
imagewidth (px)
64
64
label
class label
10 classes
image_id
stringlengths
7
25
6PermanentCrop
PermanentCrop_807
0AnnualCrop
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7Residential
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0AnnualCrop
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9SeaLake
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3Highway
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1Forest
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8River
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7Residential
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Highway_80
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Highway_804
4Industrial
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2HerbaceousVegetation
HerbaceousVegetation_815
8River
River_800
2HerbaceousVegetation
HerbaceousVegetation_563
2HerbaceousVegetation
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3Highway
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8River
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7Residential
Residential_1773
0AnnualCrop
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3Highway
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6PermanentCrop
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5Pasture
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2HerbaceousVegetation
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1Forest
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9SeaLake
SeaLake_825
9SeaLake
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5Pasture
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0AnnualCrop
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0AnnualCrop
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0AnnualCrop
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5Pasture
Pasture_1359
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3Highway
Highway_2411
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Industrial_798
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Industrial_2363
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7Residential
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7Residential
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Residential_989
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River_317
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River_1225
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Industrial_1522
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HerbaceousVegetation_1359
0AnnualCrop
AnnualCrop_774
6PermanentCrop
PermanentCrop_484
6PermanentCrop
PermanentCrop_1045
5Pasture
Pasture_1300
6PermanentCrop
PermanentCrop_159
3Highway
Highway_1983
8River
River_2002
8River
River_1354
6PermanentCrop
PermanentCrop_2465

EuroSat (RGB)

Description

A dataset based on Sentinel-2 satellite images covering 13 spectral bands and consisting of 10 classes with 27000 labeled and geo-referenced samples. This is the RGB version of the dataset with visible bands encoded as JPEG images.

The dataset does not have any default splits. Train, validation, and test splits were based on these definitions here https://github.com/google-research/google-research/blob/master/remote_sensing_representations/README.md#dataset-splits

Citation

@article{helber2019eurosat,
  title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
  author={Helber, Patrick and Bischke, Benjamin and Dengel, Andreas and Borth, Damian},
  journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  year={2019},
  publisher={IEEE}
}
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