Update README.md

README.md

@@ -2,63 +2,67 @@
 task_categories:
 - image-classification
 ---
-# BigEarthNet Dataset
+# BigEarthNet 
+**BigEarthNet** is a large-scale benchmark dataset for multi-label classification, derived from Sentinel-1 (radar) and Sentinel-2 (optical) satellite imagery. 
 
-The **BigEarthNet** dataset is a large-scale benchmark Archive for remoting sensing. The dataset contains both Sentinel-2 and Sentinel-1 imagery. 
-
-We have pre-processed the dataset by upsampling all sentinel-2 channels to 120x120 pixels and concatenated them together. Please see [Torchgeo/bigearthnet](https://github.com/microsoft/torchgeo/blob/main/torchgeo/datasets/bigearthnet.py#L385) for more information about pre-processing.
-
-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 and Sentinel-1 imagery used in the dataset:
+We have pre-processed the dataset by upsampling all sentinel-2 channels to 120x120 pixels and concatenated them together. Please see [Torchgeo/bigearthnet](https://github.com/microsoft/torchgeo/blob/main/torchgeo/datasets/bigearthnet.py#L385) for more information about pre-processing. In addition, we map the original 43 land cover classes to 19 broader categories using a predefined conversion scheme.
 
+## How to Use This Dataset
 ```python
-S2_MEAN = [752.40087073, 884.29673756, 1144.16202635, 1297.47289228, 1624.90992062, 2194.6423161, 2422.21248945, 2517.76053101, 2581.64687018, 2645.51888987, 2368.51236873, 1805.06846033]
-S2_STD = [1108.02887453, 1155.15170768, 1183.6292542, 1368.11351514, 1370.265037, 1355.55390699, 1416.51487101, 1474.78900051, 1439.3086061, 1582.28010962, 1455.52084939, 1343.48379601]
-S1_MEAN = [-12.54847273, -20.19237134]
-S1_STD = [5.25697717, 5.91150917]
+from datasets import load_dataset
 
-metadata = {
-    "s2c": {
-        "bands":["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12"],
-        "channel_wv": [442.7, 492.4, 559.8, 664.6, 704.1, 740.5, 782.8, 832.8, 864.7, 945.1, 1613.7, 2202.4],
-        "mean": S2_MEAN,
-        "std": S2_STD
-    },
-    "s1": {
-        "bands": ["VV", "VH"],
-        "channel_wv": [5500, 5700],
-        "mean": S1_MEAN,
-        "std": S1_STD
-    }
-}
+dataset = load_dataset("GFM-Bench/BigEarthNet")
+```
 
-SIZE = HEIGHT = WIDTH = 120
+Also, please see our [GFM-Bench](https://github.com/uiuctml/GFM-Bench) repository for more information about how to use the dataset! 🤗
 
-NUM_CLASSES = 19
+## Dataset Metadata
 
-spatial_resolution = 10
-```
+The following metadata provides details about the Sentinel-2 imagery used in the dataset:
+- **Number of Sentinel-1 Bands**: 2
+- **Sentinel-1 Bands**: VV, VH
+- **Number of Sentinel-2 Bands**: 12
+- **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**), B11 (**SWIR**), B12 (**SWIR**)
+- **Image Resolution**: 120 x 120 pixels
+- **Spatial Resolution**: 10 meters 
+- **Number of Classes**: 19
+- **Class Labels**: 
+    - Urban fabric
+    - Industrial or commercial units
+    - Arable land
+    - Permanent crops
+    - Pastures
+    - Complex cultivation patterns
+    - Land principally occupied by agriculture, with significant areas of natural vegetation
+    - Agro-forestry areas
+    - Broad-leaved forest
+    - Coniferous forest
+    - Mixed forest
+    - Natural grassland and sparsely vegetated areas
+    - Moors, heathland and sclerophyllous vegetation
+    - Transitional woodland, shrub
+    - Beaches, dunes, sands
+    - Inland wetlands
+    - Coastal wetlands
+    - Inland waters
+    - Marine waters
 
-## Split
-The **BigEarthNet** dataset consists splits of:
-- **train**: 269,695 samples.
-- **val**: 123,723 samples.
-- **test**: 125,866 samples.
+## Dataset Splits
+The **BigEarthNet** dataset consists following splits:
+- **train**: 269,695 samples
+- **val**: 123,723 samples
+- **test**: 125,866 samples
 
-## Features:
+## Dataset Features:
 The **BigEarthNet** dataset consists of following features:
-- **optical**: the Sentinel-2 image.
 - **radar**: the Sentinel-1 image.
+- **optical**: the Sentinel-2 image.
 - **label**: the classification label.
-- **optical_channel_wv**: the wavelength of each optical channel.
-- **radar_channel_wv**: the wavelength of each radar channel.
+- **radar_channel_wv**: the central wavelength of each Sentinel-1 bands.
+- **optical_channel_wv**: the central wavelength of each Sentinel-2 bands.
 - **spatial_resolution**: the spatial resolution of images.
-
 ## Citation
-If you use the BigEarthNet dataset in your work, please cite the original paper:
+If you use the BigEarthNet dataset in your work, please cite original papers:
 ```
 @inproceedings{sumbul2019bigearthnet,
   title={Bigearthnet: A large-scale benchmark archive for remote sensing image understanding},