id
stringclasses
5 values
split
stringclasses
3 values
text
stringclasses
5 values
0000001
train
h1
0000002
train
h2
0000003
train
h3
0000004
train
h4
0000005
train
h5
0000001
validation
h1
0000002
validation
h2
0000003
validation
h3
0000004
validation
h4
0000005
validation
h5
0000001
test
h1
0000002
test
h2
0000003
test
h3
0000004
test
h4
0000005
test
h5

cloudsen12

A dataset about clouds from Sentinel-2

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: CloudSEN12 - a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2.

ML-STAC Snippet

import mlstac
secret = 'https://huggingface.co/datasets/jfloresf/mlstac-demo/resolve/main/main.json'
train_db = mlstac.load(secret, framework='torch', stream=True, device='cpu')

Sensor: Sentinel 2 - MSI

ML-STAC Task: TensorToTensor, TensorSegmentation

Data raw repository: http://www.example.com/

Dataset discussion: https://github.com/IPL-UV/ML-STAC/discussions/2

Review mean score: 5.0

Split_strategy: random

Paper: https://www.nature.com/articles/s41597-022-01878-2

Data Providers

Name Role URL
Image & Signal Processing ['host'] https://isp.uv.es/
ESA ['producer'] https://www.esa.int/

Curators

Name Organization URL
Cesar Aybar Image & Signal Processing http://csaybar.github.io/

Reviewers

Name Organization URL Score
Cesar Aybar Image & Signal Processing http://csaybar.github.io/ 5

Labels

Name Value
clear 0
thick-cloud 1
thin-cloud 2
cloud-shadow 3

Dimensions

input

Axis Name Description
0 C Channels - Spectral bands
1 H Height
2 W Width

target

Axis Name Description
0 C Hand-crafted labels
1 H Height
2 W Width

Spectral Bands

Name Common Name Description Center Wavelength Full Width Half Max Index
B01 coastal aerosol Band 1 - Coastal aerosol - 60m 443.5 17.0 0
B02 blue Band 2 - Blue - 10m 496.5 53.0 1
B03 green Band 3 - Green - 10m 560.0 34.0 2
B04 red Band 4 - Red - 10m 664.5 29.0 3
B05 red edge 1 Band 5 - Vegetation red edge 1 - 20m 704.5 13.0 4
B06 red edge 2 Band 6 - Vegetation red edge 2 - 20m 740.5 13.0 5
B07 red edge 3 Band 7 - Vegetation red edge 3 - 20m 783.0 18.0 6
B08 NIR Band 8 - Near infrared - 10m 840.0 114.0 7
B8A red edge 4 Band 8A - Vegetation red edge 4 - 20m 864.5 19.0 8
B09 water vapor Band 9 - Water vapor - 60m 945.0 18.0 9
B10 cirrus Band 10 - Cirrus - 60m 1375.5 31.0 10
B11 SWIR 1 Band 11 - Shortwave infrared 1 - 20m 1613.5 89.0 11
B12 SWIR 2 Band 12 - Shortwave infrared 2 - 20m 2199.5 173.0 12
Downloads last month
4
Edit dataset card