---
language:
- en
tags:
  - clouds
  - sentinel-2
  - image-segmentation
  - deep-learning
  - remote-sensing
pretty_name: cloudsen12
---
# cloudsen12

***``A dataset about clouds from Sentinel-2``***

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|