Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
10K - 100K
License:
| 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} | |
| } | |
| ``` | |