
# **CloudSEN12  SCRIBBLE**
## **A Benchmark Dataset for Cloud Semantic Understanding**

![CloudSEN12 Images](https://cloudsen12.github.io/thumbnails/cloudsen12.gif)

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.

Ready to start using **[CloudSEN12](https://cloudsen12.github.io/)**?

**[Download Dataset](https://cloudsen12.github.io/download.html)**

**[Paper - Scientific Data](https://www.nature.com/articles/s41597-022-01878-2)**

**[Inference on a new S2 image](https://colab.research.google.com/github/cloudsen12/examples/blob/master/example02.ipynb)**

**[Enter to cloudApp](https://github.com/cloudsen12/CloudApp)**

**[CloudSEN12 in Google Earth Engine](https://gee-community-catalog.org/projects/cloudsen12/)**


<br>

### **Description**

<br>

| File          | Name            | Scale  | Wavelength                  | Description                                                                                           | Datatype |
|---------------|-----------------|--------|------------------------------|------------------------------------------------------------------------------------------------------|----------|
| L1C_ & L2A_   | B1              | 0.0001 | 443.9nm (S2A) / 442.3nm (S2B)| Aerosols.                                                                                            | np.int16 |
|               | B2              | 0.0001 | 496.6nm (S2A) / 492.1nm (S2B)| Blue.                                                                                                | np.int16 |
|               | B3              | 0.0001 | 560nm (S2A) / 559nm (S2B)    | Green.                                                                                               | np.int16 |
|               | B4              | 0.0001 | 664.5nm (S2A) / 665nm (S2B)  | Red.                                                                                                 | np.int16 |
|               | B5              | 0.0001 | 703.9nm (S2A) / 703.8nm (S2B)| Red Edge 1.                                                                                          | np.int16 |
|               | B6              | 0.0001 | 740.2nm (S2A) / 739.1nm (S2B)| Red Edge 2.                                                                                          | np.int16 |
|               | B7              | 0.0001 | 782.5nm (S2A) / 779.7nm (S2B)| Red Edge 3.                                                                                          | np.int16 |
|               | B8              | 0.0001 | 835.1nm (S2A) / 833nm (S2B)  | NIR.                                                                                                 | np.int16 |
|               | B8A             | 0.0001 | 864.8nm (S2A) / 864nm (S2B)  | Red Edge 4.                                                                                          | np.int16 |
|               | B9              | 0.0001 | 945nm (S2A) / 943.2nm (S2B)  | Water vapor.                                                                                         | np.int16 |
|               | B11             | 0.0001 | 1613.7nm (S2A) / 1610.4nm (S2B)| SWIR 1.                                                                                            | np.int16 |
|               | B12             | 0.0001 | 2202.4nm (S2A) / 2185.7nm (S2B)| SWIR 2.                                                                                            | np.int16 |
| L1C_          | B10             | 0.0001 | 1373.5nm (S2A) / 1376.9nm (S2B)| Cirrus.                                                                                            | np.int16 |
| L2A_          | AOT             | 0.001  | -                            | Aerosol Optical Thickness.                                                                           | np.int16 |
|               | WVP             | 0.001  | -                            | Water Vapor Pressure.                                                                                | np.int16 |
|               | TCI_R           | 1      | -                            | True Color Image, Red.                                                                               | np.int16 |
|               | TCI_G           | 1      | -                            | True Color Image, Green.                                                                             | np.int16 |
|               | TCI_B           | 1      | -                            | True Color Image, Blue.                                                                              | np.int16 |
| S1_           | VV              | 1      | 5.405GHz                     | Dual-band cross-polarization, vertical transmit/horizontal receive.                                  |np.float32|
|               | VH              | 1      | 5.405GHz                     | Single co-polarization, vertical transmit/vertical receive.                                          |np.float32|
|               | angle           | 1      | -                            | Incidence angle generated by interpolating the ‘incidenceAngle’ property.                            |np.float32|
| EXTRA_        | CDI             | 0.0001 | -                            | Cloud Displacement Index.                                                                            | np.int16 |
|               | Shwdirection    | 0.01   | -                            | Azimuth. Values range from 0°- 360°.                                                                 | np.int16 |
|               | elevation       | 1      | -                            | Elevation in meters. Obtained from MERIT Hydro datasets.                                             | np.int16 |
|               | ocurrence       | 1      | -                            | JRC Global Surface Water. The frequency with which water was present.                                | np.int16 |
|               | LC100           | 1      | -                            | Copernicus land cover product. CGLS-LC100 Collection 3.                                              | np.int16 |
|               | LC10            | 1      | -                            | ESA WorldCover 10m v100 product.                                                                     | np.int16 |
| LABEL_        | fmask           | 1      | -                            | Fmask4.0 cloud masking.                                                                              | np.int16 |
|               | QA60            | 1      | -                            | SEN2 Level-1C cloud mask.                                                                            | np.int8  |
|               | s2cloudless     | 1      | -                            | sen2cloudless results.                                                                               | np.int8  |
|               | sen2cor         | 1      | -                            | Scene Classification band. Obtained from SEN2 level 2A.                                              | np.int8  |
|               | cd_fcnn_rgbi    | 1      | -                            | López-Puigdollers et al. results based on RGBI bands.                                                | np.int8  |
|               |cd_fcnn_rgbi_swir| 1      | -                            | López-Puigdollers et al. results based on RGBISWIR bands.                                            | np.int8  |
|               | kappamask_L1C   | 1      | -                            | KappaMask results using SEN2 level L1C as input.                                                     | np.int8  |
|               | kappamask_L2A   | 1      | -                            | KappaMask results using SEN2 level L2A as input.                                                     | np.int8  |
|               | manual_hq       | 1      |                              | High-quality pixel-wise manual annotation.                                                           | np.int8  |
|               | manual_sc       | 1      |                              | Scribble manual annotation.                                                                          | np.int8  |

<br>


### **Label Description**


| **CloudSEN12**   | **KappaMask**     | **Sen2Cor**             | **Fmask**        | **s2cloudless**       | **CD-FCNN**         | **QA60**           |
|------------------|------------------|-------------------------|-----------------|-----------------------|---------------------|--------------------|
| 0 Clear          | 1 Clear           | 4 Vegetation            | 0 Clear land    | 0 Clear               | 0 Clear             | 0 Clear            |
|                  |                  | 2 Dark area pixels      | 1 Clear water   |                       |                     |                    |
|                  |                  | 5 Bare Soils            | 3 Snow          |                       |                     |                    |
|                  |                  | 6 Water                 |                 |                       |                     |                    |
|                  |                  | 11 Snow                 |                 |                       |                     |                    |
| 1 Thick cloud    | 4 Cloud           | 8 Cloud medium probability | 4 Cloud         | 1 Cloud               | 1 Cloud             | 1024 Opaque cloud  |
|                  |                  | 9 Cloud high probability   |                 |                       |                     |                    |
| 2 Thin cloud     | 3 Semi-transparent cloud | 10 Thin cirrus   |                 |                       |                     | 2048 Cirrus cloud  |
| 3 Cloud shadow   | 2 Cloud shadow    | 3 Cloud shadows         | 2 Cloud shadow  |                       |                     |                    |



<br>


### **np.memmap shape information**

<br>

**train shape: (8785, 512, 512)**
<br>
**val shape: (560, 512, 512)**
<br>
**test shape: (655, 512, 512)**

<br>

### **Example**

<br>

```py
import numpy as np

# Read high-quality train
train_shape = (8785, 512, 512)
B4X = np.memmap('train/L1C_B04.dat', dtype='int16', mode='r', shape=train_shape)
y = np.memmap('train/manual_hq.dat', dtype='int8', mode='r', shape=train_shape)

# Read high-quality val
val_shape = (560, 512, 512)
B4X = np.memmap('val/L1C_B04.dat', dtype='int16', mode='r', shape=val_shape)
y = np.memmap('val/manual_hq.dat', dtype='int8', mode='r', shape=val_shape)


# Read high-quality test
test_shape = (655, 512, 512)
B4X = np.memmap('test/L1C_B04.dat', dtype='int16', mode='r', shape=test_shape)
y = np.memmap('test/manual_hq.dat', dtype='int8', mode='r', shape=test_shape)
```
<br>


This work has been partially supported by the Spanish Ministry of Science and Innovation project 
PID2019-109026RB-I00 (MINECO-ERDF) and the Austrian Space Applications Programme within the 
**[SemantiX project](https://austria-in-space.at/en/projects/2019/semantix.php)**.

license:cc-by-nc-4.0 region:us
{"license": "cc-by-nc-4.0"}
TAGS
#license-cc-by-nc-4.0 #region-us 

CloudSEN12 SCRIBBLE
===================


A Benchmark Dataset for Cloud Semantic Understanding
----------------------------------------------------


!CloudSEN12 Images


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.


Ready to start using CloudSEN12?


Download Dataset


Paper - Scientific Data


Inference on a new S2 image


Enter to cloudApp


CloudSEN12 in Google Earth Engine


  

### Description


  


  

### Label Description



  

### URL shape information


  

train shape: (8785, 512, 512)
  

val shape: (560, 512, 512)
  

test shape: (655, 512, 512)


  

### Example


  

  

This work has been partially supported by the Spanish Ministry of Science and Innovation project
PID2019-109026RB-I00 (MINECO-ERDF) and the Austrian Space Applications Programme within the
SemantiX project.



### Description ### Label Description ### URL shape information


  

train shape: (8785, 512, 512)
  

val shape: (560, 512, 512)
  

test shape: (655, 512, 512) ### Example


  

  

This work has been partially supported by the Spanish Ministry of Science and Innovation project
PID2019-109026RB-I00 (MINECO-ERDF) and the Austrian Space Applications Programme within the
SemantiX project.
TAGS
#license-cc-by-nc-4.0 #region-us 
 ### Description ### Label Description ### URL shape information


  

train shape: (8785, 512, 512)
  

val shape: (560, 512, 512)
  

test shape: (655, 512, 512) ### Example


  

  

This work has been partially supported by the Spanish Ministry of Science and Innovation project
PID2019-109026RB-I00 (MINECO-ERDF) and the Austrian Space Applications Programme within the
SemantiX project.
passage: TAGS
#license-cc-by-nc-4.0 #region-us 
### Description### Label Description### URL shape information


  

train shape: (8785, 512, 512)
  

val shape: (560, 512, 512)
  

test shape: (655, 512, 512)### Example


  

  

This work has been partially supported by the Spanish Ministry of Science and Innovation project
PID2019-109026RB-I00 (MINECO-ERDF) and the Austrian Space Applications Programme within the
SemantiX project.