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.
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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 |
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.