image
image
label
class label
10 classes
filename
stringlengths
11
29
0Annual Crop
AnnualCrop_1.tif
0Annual Crop
AnnualCrop_10.tif
0Annual Crop
AnnualCrop_100.tif
0Annual Crop
AnnualCrop_1000.tif
0Annual Crop
AnnualCrop_1001.tif
0Annual Crop
AnnualCrop_1004.tif
0Annual Crop
AnnualCrop_1005.tif
0Annual Crop
AnnualCrop_1006.tif
0Annual Crop
AnnualCrop_1009.tif
0Annual Crop
AnnualCrop_1010.tif
0Annual Crop
AnnualCrop_1011.tif
0Annual Crop
AnnualCrop_1013.tif
0Annual Crop
AnnualCrop_1016.tif
0Annual Crop
AnnualCrop_1018.tif
0Annual Crop
AnnualCrop_1019.tif
0Annual Crop
AnnualCrop_1020.tif
0Annual Crop
AnnualCrop_1021.tif
0Annual Crop
AnnualCrop_1022.tif
0Annual Crop
AnnualCrop_1023.tif
0Annual Crop
AnnualCrop_1026.tif
0Annual Crop
AnnualCrop_1027.tif
0Annual Crop
AnnualCrop_1028.tif
0Annual Crop
AnnualCrop_1029.tif
0Annual Crop
AnnualCrop_103.tif
0Annual Crop
AnnualCrop_1031.tif
0Annual Crop
AnnualCrop_1032.tif
0Annual Crop
AnnualCrop_1033.tif
0Annual Crop
AnnualCrop_1034.tif
0Annual Crop
AnnualCrop_1035.tif
0Annual Crop
AnnualCrop_1036.tif
0Annual Crop
AnnualCrop_1037.tif
0Annual Crop
AnnualCrop_1038.tif
0Annual Crop
AnnualCrop_104.tif
0Annual Crop
AnnualCrop_1040.tif
0Annual Crop
AnnualCrop_1042.tif
0Annual Crop
AnnualCrop_1043.tif
0Annual Crop
AnnualCrop_1045.tif
0Annual Crop
AnnualCrop_1046.tif
0Annual Crop
AnnualCrop_1048.tif
0Annual Crop
AnnualCrop_1049.tif
0Annual Crop
AnnualCrop_1050.tif
0Annual Crop
AnnualCrop_1051.tif
0Annual Crop
AnnualCrop_1053.tif
0Annual Crop
AnnualCrop_1054.tif
0Annual Crop
AnnualCrop_1055.tif
0Annual Crop
AnnualCrop_1056.tif
0Annual Crop
AnnualCrop_1058.tif
0Annual Crop
AnnualCrop_106.tif
0Annual Crop
AnnualCrop_1060.tif
0Annual Crop
AnnualCrop_1062.tif
0Annual Crop
AnnualCrop_1064.tif
0Annual Crop
AnnualCrop_1066.tif
0Annual Crop
AnnualCrop_1068.tif
0Annual Crop
AnnualCrop_1069.tif
0Annual Crop
AnnualCrop_1070.tif
0Annual Crop
AnnualCrop_1073.tif
0Annual Crop
AnnualCrop_1074.tif
0Annual Crop
AnnualCrop_1076.tif
0Annual Crop
AnnualCrop_1077.tif
0Annual Crop
AnnualCrop_108.tif
0Annual Crop
AnnualCrop_1080.tif
0Annual Crop
AnnualCrop_1082.tif
0Annual Crop
AnnualCrop_1084.tif
0Annual Crop
AnnualCrop_1086.tif
0Annual Crop
AnnualCrop_1087.tif
0Annual Crop
AnnualCrop_1089.tif
0Annual Crop
AnnualCrop_1091.tif
0Annual Crop
AnnualCrop_1092.tif
0Annual Crop
AnnualCrop_1094.tif
0Annual Crop
AnnualCrop_1095.tif
0Annual Crop
AnnualCrop_1097.tif
0Annual Crop
AnnualCrop_11.tif
0Annual Crop
AnnualCrop_110.tif
0Annual Crop
AnnualCrop_1102.tif
0Annual Crop
AnnualCrop_1104.tif
0Annual Crop
AnnualCrop_1105.tif
0Annual Crop
AnnualCrop_1106.tif
0Annual Crop
AnnualCrop_1107.tif
0Annual Crop
AnnualCrop_1108.tif
0Annual Crop
AnnualCrop_1109.tif
0Annual Crop
AnnualCrop_111.tif
0Annual Crop
AnnualCrop_1111.tif
0Annual Crop
AnnualCrop_1112.tif
0Annual Crop
AnnualCrop_1114.tif
0Annual Crop
AnnualCrop_1116.tif
0Annual Crop
AnnualCrop_1118.tif
0Annual Crop
AnnualCrop_1119.tif
0Annual Crop
AnnualCrop_1126.tif
0Annual Crop
AnnualCrop_1127.tif
0Annual Crop
AnnualCrop_113.tif
0Annual Crop
AnnualCrop_1132.tif
0Annual Crop
AnnualCrop_1134.tif
0Annual Crop
AnnualCrop_1136.tif
0Annual Crop
AnnualCrop_1137.tif
0Annual Crop
AnnualCrop_1138.tif
0Annual Crop
AnnualCrop_114.tif
0Annual Crop
AnnualCrop_1140.tif
0Annual Crop
AnnualCrop_1141.tif
0Annual Crop
AnnualCrop_1142.tif
0Annual Crop
AnnualCrop_1143.tif

EuroSAT RGB

EuroSAT RGB

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

Description

The EuroSAT dataset is a comprehensive land cover classification dataset that focuses on images taken by the ESA Sentinel-2 satellite. It contains a total of 27,000 images, each with a resolution of 64x64 pixels. These images cover 10 distinct land cover classes and are collected from over 34 European countries.

The dataset is available in two versions: RGB only (this repo) and all 13 Multispectral (MS) Sentinel-2 bands. EuroSAT is considered a relatively easy dataset, with approximately 98.6% accuracy achievable using a ResNet-50 architecture.

  • Total Number of Images: 27000
  • Bands: 3 (RGB)
  • Image Resolution: 64x64m
  • Land Cover Classes: 10
  • Classes: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake

Usage

To use this dataset, simply use datasets.load_dataset("blanchon/EuroSAT_RGB").

from datasets import load_dataset
EuroSAT_RGB = load_dataset("blanchon/EuroSAT_RGB")

Citation

If you use the EuroSAT dataset in your research, please consider citing the following publication:

@article{helber2017eurosat,
   title={EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification},
   author={Helber, et al.},
   journal={ArXiv preprint arXiv:1709.00029},
   year={2017}
}
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