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README.md

@@ -1,43 +1,98 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - 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: 65346484
-    num_examples: 18900
-  - name: validation
-    num_bytes: 18899916
-    num_examples: 5400
-  - name: test
-    num_bytes: 9310880
-    num_examples: 2700
-  download_size: 88578372
-  dataset_size: 93557280
-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
+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}
+}
+```