Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K - 100K
License:
File size: 2,467 Bytes
65d308b 48a5cd8 65d308b 48a5cd8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | ---
license: mit
task_categories:
- image-classification
task_ids:
- multi-class-image-classification
language:
- en
tags:
- remote-sensing
- satellite-imagery
- land-use
- land-cover
- sentinel-2
- earth-observation
- eurosat
pretty_name: EuroSAT RGB
size_categories:
- 10K<n<100K
source_datasets:
- original
---
# EuroSAT RGB
## Dataset Description
EuroSAT is a dataset for land use and land cover (LULC) classification using Sentinel-2 satellite imagery. This version contains the RGB (visible spectrum) bands encoded as JPEG images at 64x64 pixel resolution.
The dataset covers 10 land use/land cover classes across 27,000 geo-referenced images from 34 European countries.
- **Source:** <https://zenodo.org/records/7711810>
- **DOI:** [10.5281/zenodo.7711810](https://doi.org/10.5281/zenodo.7711810)
- **License:** MIT
- **Paper:** [EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification](https://doi.org/10.1109/JSTARS.2019.2918242)
## Authors
Patrick Helber, Benjamin Bischke, Andreas Dengel, Damian Borth
## Dataset Structure
### Splits
| Split | Examples |
|------------|----------|
| train | 18,900 |
| validation | 5,400 |
| test | 2,700 |
### Classes
| Label | Class Name |
|-------|-----------------------|
| 0 | AnnualCrop |
| 1 | Forest |
| 2 | HerbaceousVegetation |
| 3 | Highway |
| 4 | Industrial |
| 5 | Pasture |
| 6 | PermanentCrop |
| 7 | Residential |
| 8 | River |
| 9 | SeaLake |
### Features
- `image`: 64x64 RGB JPEG satellite image
- `label`: Integer class label (0–9)
- `filename`: Original filename with class directory prefix
## Usage
```python
from datasets import load_dataset
dataset = load_dataset("giswqs/EuroSAT_RGB")
# Access training split
train = dataset["train"]
print(train[0])
```
## Citation
```bibtex
@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},
volume={12},
number={7},
pages={2217--2226},
year={2019},
doi={10.1109/JSTARS.2019.2918242},
publisher={IEEE}
}
```
|