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Datasets:
giswqs
/
EuroSAT_MS

Tasks:
Image Classification
Modalities:
Text
Geospatial
Formats:
parquet
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K - 100K
Tags:
remote-sensing
satellite-imagery
land-use
land-cover
sentinel-2
earth-observation
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
EuroSAT_MS
File size: 4,560 Bytes
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---
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
  - multispectral
pretty_name: EuroSAT Multispectral
size_categories:
  - 10K<n<100K
source_datasets:
  - original
---

# EuroSAT Multispectral (All 13 Sentinel-2 Bands)

## Dataset Description

EuroSAT is a dataset for land use and land cover (LULC) classification using Sentinel-2 satellite imagery. This version contains **all 13 Sentinel-2 spectral bands** stored as uint16 arrays at 64x64 pixel resolution.

The dataset covers 10 land use/land cover classes across 26,998 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)
- **RGB Version:** [giswqs/EuroSAT_RGB](https://huggingface.co/datasets/giswqs/EuroSAT_RGB)

## Authors

Patrick Helber, Benjamin Bischke, Andreas Dengel, Damian Borth

## Spectral Bands

Each image is a numpy array of shape `(13, 64, 64)` with dtype `uint16`. The 13 bands correspond to the Sentinel-2 spectral bands:

| Index | Band | Sentinel-2 Band  | Wavelength (nm) | Resolution (m) |
|-------|------|-------------------|-----------------|----------------|
| 0     | B01  | Coastal aerosol   | 443             | 60             |
| 1     | B02  | Blue              | 490             | 10             |
| 2     | B03  | Green             | 560             | 10             |
| 3     | B04  | Red               | 665             | 10             |
| 4     | B05  | Veg. Red Edge 1   | 705             | 20             |
| 5     | B06  | Veg. Red Edge 2   | 740             | 20             |
| 6     | B07  | Veg. Red Edge 3   | 783             | 20             |
| 7     | B08  | NIR               | 842             | 10             |
| 8     | B08A | Narrow NIR        | 865             | 20             |
| 9     | B09  | Water Vapour      | 945             | 60             |
| 10    | B10  | SWIR - Cirrus     | 1375            | 60             |
| 11    | B11  | SWIR 1            | 1610            | 20             |
| 12    | B12  | SWIR 2            | 2190            | 20             |

> **Note:** All bands are resampled to 10m resolution (64x64 pixels) in the original dataset.

## Dataset Structure

### Splits

| Split      | Examples |
|------------|----------|
| train      | 18,880   |
| validation | 5,405    |
| test       | 2,713    |

### 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`: `Array3D(shape=(13, 64, 64), dtype="uint16")` — 13-band Sentinel-2 multispectral image
- `label`: `ClassLabel` — Integer class label (0–9)
- `filename`: `Value("string")` — Original filename with class directory prefix

## Usage

```python
from datasets import load_dataset
import numpy as np

dataset = load_dataset("giswqs/EuroSAT_MS")

# Access training split
train = dataset["train"]
sample = train[0]

# Get multispectral image as numpy array
image = np.array(sample["image"], dtype=np.uint16)  # shape: (13, 64, 64)
label = sample["label"]
filename = sample["filename"]

print(f"Image shape: {image.shape}, dtype: {image.dtype}")
print(f"Label: {label}, Filename: {filename}")

# Extract RGB bands (B04, B03, B02 = indices 3, 2, 1)
rgb = image[[3, 2, 1]]  # shape: (3, 64, 64)

# Compute NDVI
red = image[3].astype(np.float32)
nir = image[7].astype(np.float32)
ndvi = (nir - red) / (nir + red + 1e-8)
```

## 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}
}
```