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README.md

@@ -1,49 +1,139 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype:
-      array3_d:
-        shape:
-        - 13
-        - 64
-        - 64
-        dtype: uint16
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': AnnualCrop
-          '1': Forest
-          '2': HerbaceousVegetation
-          '3': Highway
-          '4': Industrial
-          '5': Pasture
-          '6': PermanentCrop
-          '7': Residential
-          '8': River
-          '9': SeaLake
-  - name: filename
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 2075310656
-    num_examples: 18880
-  - name: validation
-    num_bytes: 594123524
-    num_examples: 5405
-  - name: test
-    num_bytes: 298215919
-    num_examples: 2713
-  download_size: 2246047503
-  dataset_size: 2967650099
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: validation
-    path: data/validation-*
-  - split: test
-    path: data/test-*
+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}
+}
+```