Update README.md

README.md

@@ -2,57 +2,42 @@
 task_categories:
 - image-classification
 ---
-# EuroSAT Dataset
-
-The **EuroSAT** dataset consists of satellite imagery for land use and land cover classification. It contains labeled images of 10 different land cover classes.
-
-Please see our [GFM-Bench](https://github.com/uiuctml/GFM-Bench) for more information about how to use the dataset! 🙂
-
-## Metadata
-
-The following metadata provides details about the Sentinel-2 imagery used in the dataset:
+# EuroSAT 
+**EuroSAT** is a benchmark dataset for land use and land cover classification based on Sentinel-2 satellite imagery. It contains 27,000 labeled images covering 10 classes (e.g., agricultural, residential, industrial, and forest areas). The dataset features multi-spectral bands with a spatial resolution of 10 meters per pixel and an image resolution of 64 × 64 pixels. 
 
+## How to Use This Dataset
 ```python
-S2_MEAN = [1354.40546513, 1118.24399958, 1042.92983953, 947.62620298, 1199.47283961, 1999.79090914, 2369.22292565, 2296.82608323, 732.08340178, 12.11327804, 1819.01027855, 1118.92391149, 2594.14080798]
-S2_STD = [245.71762908, 333.00778264, 395.09249139, 593.75055589, 566.4170017, 861.18399006, 1086.63139075, 1117.98170791, 404.91978886, 4.77584468, 1002.58768311, 761.30323499, 1231.58581042]
+from datasets import load_dataset
 
-metadata = {
-    "s2c": {
-        "bands": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B10", "B11", "B12"],
-        "channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 945.1, 1373.5, 1613.7, 2202.4],
-        "mean": S2_MEAN,
-        "std": S2_STD,
-    },
-    "s1": {
-        "bands": None,
-        "channel_wv": None,
-        "mean": None,
-        "std": None   
-    }
-}
+dataset = load_dataset("GFM-Bench/EuroSAT")
+```
 
-SIZE = HEIGHT = WIDTH = 64
+Also, please see our [GFM-Bench](https://github.com/uiuctml/GFM-Bench) repository for more information about how to use the dataset! 🤗
 
-NUM_CLASSES = 10
+## Dataset Metadata
 
-spatial_resolution = 10
-```
-
-## Split
-The **EuroSAT** dataset consists splits of:
+The following metadata provides details about the Sentinel-2 imagery used in the dataset:
+- **Number of Sentinel-2 Bands**: 13
+- **Sentinel-2 Bands**: B01 (**Coastal aerosol**), B02 (**Blue**), B03 (**Green**), B04 (**Red**), B05 (**Vegetation red edge**), B06 (**Vegetation red edge**), B07 (**Vegetation red edge**), B08 (**NIR**), B8A (**Narrow NIR**), B09 (**Water vapour**), B10 (**SWIR – Cirrus**), B11 (**SWIR**), B12 (**SWIR**)
+- **Image Resolution**: 64 x 64 pixels
+- **Spatial Resolution**: 10 meters 
+- **Number of Classes**: 10 
+- **Class Labels**: Annual Crop, Forest, Herbaceous Vegetation, Highway, Industrial Buildings, Pasture, Permanent Crop, Residential Buildings, River, SeaLake
+
+## Dataset Splits
+The **EuroSAT** dataset consists following splits:
 - **train**: 16200 samples
 - **val**: 5400 samples
 - **test**: 5400 samples
 
-## Features:
+## Dataset Features:
 The **EuroSAT** dataset consists of following features:
 - **optical**: the Sentinel-2 image.
 - **label**: the classification label.
-- **optical_channel_wv**: the wavelength of each optical channel.
+- **optical_channel_wv**: the central wavelength of each optical channel.
 - **spatial_resolution**: the spatial resolution of images.
-
 ## Citation
-If you use the EuroSAT dataset in your work, please cite the original paper:
+If you use the EuroSAT dataset in your work, please cite original papers:
 ```
 @article{helber2019eurosat,
   title={Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification},
@@ -64,4 +49,15 @@ If you use the EuroSAT dataset in your work, please cite the original paper:
   year={2019},
   publisher={IEEE}
 }
+```
+and
+```
+@inproceedings{helber2018introducing,
+  title={Introducing 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},
+  booktitle={IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium},
+  pages={204--207},
+  year={2018},
+  organization={IEEE}
+}
 ```