license: cc-by-nc-4.0
CloudSEN12
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?
CloudSEN12 in Google Earth Engine
Example
import numpy as np
# Read high-quality train
train_shape = (8490, 512, 512)
B4X = np.memmap('train_B04.dat', dtype='int16', mode='r', shape=train_shape)
y = np.memmap('train_target.dat', dtype='int8', mode='r', shape=train_shape)
# Read high-quality val
val_shape = (535, 512, 512)
B4X = np.memmap('val_B04.dat', dtype='int16', mode='r', shape=val_shape)
y = np.memmap('val_target.dat', dtype='int8', mode='r', shape=val_shape)
# Read high-quality test
test_shape = (975, 512, 512)
B4X = np.memmap('test_B04.dat', dtype='int16', mode='r', shape=test_shape)
y = np.memmap('test_target.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.