---
license: cc-by-nc-4.0
---
# **CloudSEN12 SCRIBBLE**
## **A Benchmark Dataset for Cloud Semantic Understanding**
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/)**
### **Description**
| 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 |
### **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 | | | |
### **np.memmap shape information**
**train shape: (8785, 512, 512)**
**val shape: (560, 512, 512)**
**test shape: (655, 512, 512)**
### **Example**
```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)
```
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)**.