Datasets:
image
imagewidth (px) 64
64
| 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 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.
- Paper: https://arxiv.org/abs/1709.00029
- Homepage: https://github.com/phelber/EuroSAT
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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