Update README.md

README.md

@@ -4,11 +4,99 @@ language:
 tags:
   - clouds
   - sentinel-2
+  - image-segmentation
+  - deep-learning
   - remote-sensing
-pretty_name: cloudSEN12
+pretty_name: cloudsen12
 ---
-# cloudSEN12
+# cloudsen12
 
 ***``A dataset about clouds from Sentinel-2``***
 
-cloudSEN12 is a dataset about clouds from Sentinel-2. It contains 4 classes: clear, thin, thick and overcast. The dataset is composed of 1800 images of 64x64 pixels. The dataset is divided into 3 splits: train, validation and test.
+CloudSEN12 is a LARGE dataset (~1 TB) for cloud semantic understanding that consists of 49,400 image patches (IP) that are evenly spread  throughout all continents except Antarctica. Each IP covers 5090 x 5090 meters and contains data from Sentinel-2 levels 1C and 2A, hand-crafted  annotations of thick and thin clouds and cloud shadows, Sentinel-1 Synthetic Aperture Radar (SAR), digital elevation model, surface water occurrence, land cover classes, and cloud mask results from six cutting-edge cloud detection algorithms.
+ CloudSEN12 is designed to support both weakly and self-/semi-supervised learning strategies by including three distinct forms of hand-crafted labeling data: high-quality, scribble and no-annotation. For more details on how we created the dataset see our paper: CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.
+
+
+**ML-STAC Snippet**
+```python
+import mlstac
+secret = 'https://huggingface.co/datasets/jfloresf/mlstac-demo/resolve/main/main.json'
+train_db = mlstac.load(secret, framework='torch', stream=True, device='cpu')
+```
+
+**Sensor: Sentinel 2 - MSI**
+
+**ML-STAC Task: TensorToTensor, TensorSegmentation**
+
+**Data raw repository:  [http://www.example.com/](http://www.example.com/)**
+
+**Dataset discussion:  [https://github.com/IPL-UV/ML-STAC/discussions/2](https://github.com/IPL-UV/ML-STAC/discussions/2)**
+
+**Review mean score:  5.0**
+
+**Split_strategy:  random**
+
+**Paper:  [https://www.nature.com/articles/s41597-022-01878-2](https://www.nature.com/articles/s41597-022-01878-2)**
+## Data Providers
+
+|Name|Role|URL|
+| :---: | :---: | :---: |
+|Image & Signal Processing|['host']|https://isp.uv.es/|
+|ESA|['producer']|https://www.esa.int/|
+
+## Curators
+
+|Name|Organization|URL|
+| :---: | :---: | :---: |
+|Cesar Aybar|Image & Signal Processing|http://csaybar.github.io/|
+
+## Reviewers
+
+|Name|Organization|URL|Score|
+| :---: | :---: | :---: | :---: |
+|Cesar Aybar|Image & Signal Processing|http://csaybar.github.io/|5|
+
+## Labels
+
+|Name|Value|
+| :---: | :---: |
+|clear|0|
+|thick-cloud|1|
+|thin-cloud|2|
+|cloud-shadow|3|
+
+## Dimensions
+
+### input
+
+|Axis|Name|Description|
+| :---: | :---: | :---: |
+|0|C|Channels - Spectral bands|
+|1|H|Height|
+|2|W|Width|
+
+### target
+
+|Axis|Name|Description|
+| :---: | :---: | :---: |
+|0|C|Hand-crafted labels|
+|1|H|Height|
+|2|W|Width|
+
+## Spectral Bands
+
+|Name|Common Name|Description|Center Wavelength|Full Width Half Max|Index|
+| :---: | :---: | :---: | :---: | :---: | :---: |
+|B01|coastal aerosol|Band 1 - Coastal aerosol - 60m|443.5|17.0|0|
+|B02|blue|Band 2 - Blue - 10m|496.5|53.0|1|
+|B03|green|Band 3 - Green - 10m|560.0|34.0|2|
+|B04|red|Band 4 - Red - 10m|664.5|29.0|3|
+|B05|red edge 1|Band 5 - Vegetation red edge 1 - 20m|704.5|13.0|4|
+|B06|red edge 2|Band 6 - Vegetation red edge 2 - 20m|740.5|13.0|5|
+|B07|red edge 3|Band 7 - Vegetation red edge 3 - 20m|783.0|18.0|6|
+|B08|NIR|Band 8 - Near infrared - 10m|840.0|114.0|7|
+|B8A|red edge 4|Band 8A - Vegetation red edge 4 - 20m|864.5|19.0|8|
+|B09|water vapor|Band 9 - Water vapor - 60m|945.0|18.0|9|
+|B10|cirrus|Band 10 - Cirrus - 60m|1375.5|31.0|10|
+|B11|SWIR 1|Band 11 - Shortwave infrared 1 - 20m|1613.5|89.0|11|
+|B12|SWIR 2|Band 12 - Shortwave infrared 2 - 20m|2199.5|173.0|12|