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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?

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Paper - Scientific Data

Inference on a new S2 image

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CloudSEN12 in Google Earth Engine


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


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.