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drawing

CloudSEN12+ is a significant extension of the CloudSEN12 dataset, which doubles the number of expert-reviewed labels, making it, by a large margin, the largest cloud detection dataset to date for Sentinel-2. All labels from the previous version have been curated and refined, enhancing the dataset's trustworthiness. This new release is licensed under CC0, which puts it in the public domain and allows anyone to use, modify, and distribute it without permission or attribution.

Data Folder order

The CloudSEN12+ dataset is organized into train, val, and test splits. The images have been padded from 509x509 to 512x512 and 2000x2000 to 2048x2048 to ensure that the patches are divisible by 32. The padding is filled with zeros in the left and bottom sides of the image. For those who prefer traditional storage formats, GeoTIFF files are available in our ScienceDataBank repository.

drawing

CloudSEN12+ spatial coverage. The terms p509 and p2000 denote the patch size 509 × 509 and 2000 × 2000, respectively. ‘high’, ‘scribble’, and ‘nolabel’ refer to the types of expert-labeled annotations

ML-STAC Snippet

import mlstac
dataset = mlstac.load('isp-uv-es/CloudSEN12Plus')

Sensor: Sentinel2 - MSI

ML-STAC Task: image-segmentation

ML-STAC Dataset Version: 1.0.0

Data raw repository: https://cloudsen12.github.io/

Dataset discussion: https://huggingface.co/datasets/isp-uv-es/CloudSEN12Plus/discussions

Split_strategy: stratified

Paper: https://www.sciencedirect.com/science/article/pii/S2352340924008163

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/

Labels

For human high-quality labels (also UnetMobV2_V2 & UnetMobV2_V1 predictions).

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

For human scribble labels.

Name Value
clear 0
thick-cloud border 1
thick-cloud center 2
thin-cloud border 3
thin-cloud center 4
cloud-shadow border 5
cloud-shadow center 6

Dimensions

Axis Name Description
0 C Spectral bands
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
CM1 Cloud Mask 1 Expert-labeled image. - - 13
CM2 Cloud Mask 2 UnetMobV2-V1 labeled image. - - 14

Data Structure

We use .mls format to store the data in HugginFace and GeoTIFF for ScienceDataBank.

Folder Structure

The fixed/ folder contains high and scribble labels, which have been improved in this new version. These changes have already been integrated.

The demo/ folder contains examples illustrating how to utilize the models trained with CLoudSEN12 to estimate the hardness and trustworthiness indices.

The images/ folder contains the CloudSEN12+ imagery

Download

The code below can be used to download the dataset using the mlstac library. For a more detailed example, please refer to the examples section in our website https://cloudsen12.github.io/.

import mlstac
import matplotlib.pyplot as plt
import numpy as np

ds = mlstac.load(snippet="isp-uv-es/CloudSEN12Plus")
subset = ds.metadata[(ds.metadata["split"] == "test") & (ds.metadata["label_type"] == "high") & (ds.metadata["proj_shape"] == 509)][10:14]
datacube = mlstac.get_data(dataset=subset)

Make a plot of the data point downloaded

datapoint = datacube[2]
datapoint_rgb = np.moveaxis(datapoint[[3, 2, 1]], 0, -1) / 5_000
fig, ax = plt.subplots(1, 3, figsize=(10, 5))
ax[0].imshow(datapoint_rgb)
ax[0].set_title("RGB")
ax[1].imshow(datapoint[13], cmap="gray")
ax[1].set_title("Human label")
ax[2].imshow(datapoint[14], cmap="gray")
ax[2].set_title("UnetMobV2 v1.0")

image/png

Citation

Cite the dataset as:

@article{aybar2024cloudsen12+,
  title={CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2},
  author={Aybar, Cesar and Bautista, Lesly and Montero, David and Contreras, Julio and Ayala, Daryl and Prudencio, Fernando and Loja, Jhomira and Ysuhuaylas, Luis and Herrera, Fernando and Gonzales, Karen and others},
  journal={Data in Brief},
  pages={110852},
  year={2024},
  DOI={10.1016/j.dib.2024.110852},
  publisher={Elsevier}
}
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