image
imagewidth (px)
64
64
label
class label
10 classes
image_id
stringlengths
7
25
6PermanentCrop
PermanentCrop_807
0AnnualCrop
AnnualCrop_889
3Highway
Highway_1169
4Industrial
Industrial_998
0AnnualCrop
AnnualCrop_2077
6PermanentCrop
PermanentCrop_1629
0AnnualCrop
AnnualCrop_2642
9SeaLake
SeaLake_2469
9SeaLake
SeaLake_2906
5Pasture
Pasture_1947
8River
River_1535
3Highway
Highway_1108
1Forest
Forest_2786
2HerbaceousVegetation
HerbaceousVegetation_2134
3Highway
Highway_1667
1Forest
Forest_218
1Forest
Forest_1441
7Residential
Residential_2005
6PermanentCrop
PermanentCrop_1055
0AnnualCrop
AnnualCrop_431
9SeaLake
SeaLake_1933
3Highway
Highway_411
1Forest
Forest_2798
8River
River_2436
7Residential
Residential_1540
5Pasture
Pasture_1708
3Highway
Highway_80
3Highway
Highway_804
4Industrial
Industrial_536
2HerbaceousVegetation
HerbaceousVegetation_815
8River
River_800
2HerbaceousVegetation
HerbaceousVegetation_563
2HerbaceousVegetation
HerbaceousVegetation_91
3Highway
Highway_1759
8River
River_903
7Residential
Residential_1773
0AnnualCrop
AnnualCrop_1569
1Forest
Forest_2998
3Highway
Highway_615
6PermanentCrop
PermanentCrop_1285
5Pasture
Pasture_1436
2HerbaceousVegetation
HerbaceousVegetation_587
1Forest
Forest_1742
5Pasture
Pasture_73
9SeaLake
SeaLake_825
9SeaLake
SeaLake_976
5Pasture
Pasture_1503
0AnnualCrop
AnnualCrop_1643
0AnnualCrop
AnnualCrop_1089
0AnnualCrop
AnnualCrop_732
5Pasture
Pasture_1359
0AnnualCrop
AnnualCrop_826
0AnnualCrop
AnnualCrop_1702
3Highway
Highway_2411
0AnnualCrop
AnnualCrop_25
8River
River_1637
0AnnualCrop
AnnualCrop_2402
0AnnualCrop
AnnualCrop_827
6PermanentCrop
PermanentCrop_2406
3Highway
Highway_221
3Highway
Highway_529
5Pasture
Pasture_717
4Industrial
Industrial_77
3Highway
Highway_22
6PermanentCrop
PermanentCrop_2201
5Pasture
Pasture_789
4Industrial
Industrial_798
0AnnualCrop
AnnualCrop_2566
1Forest
Forest_1052
4Industrial
Industrial_2363
3Highway
Highway_2258
9SeaLake
SeaLake_684
7Residential
Residential_765
4Industrial
Industrial_1991
9SeaLake
SeaLake_1339
4Industrial
Industrial_520
7Residential
Residential_196
2HerbaceousVegetation
HerbaceousVegetation_970
7Residential
Residential_1529
1Forest
Forest_706
7Residential
Residential_2765
9SeaLake
SeaLake_2046
9SeaLake
SeaLake_2080
5Pasture
Pasture_607
1Forest
Forest_2441
7Residential
Residential_989
8River
River_317
0AnnualCrop
AnnualCrop_96
8River
River_1225
4Industrial
Industrial_1522
2HerbaceousVegetation
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}
}
Downloads last month
147
Edit dataset card

Models trained or fine-tuned on timm/eurosat-rgb

Collection including timm/eurosat-rgb