metadata

license:
  - cc0-1.0
language:
  - en
tags:
  - clouds
  - sentinel-2
  - image-segmentation
  - deep-learning
  - remote-sensing
pretty_name: cloudsen12plus

cloudsen12plus

The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2

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. 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.

*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*

🌮 TACO Snippet

Load this dataset using the tacoreader library.

import tacoreader
dataset = tacoreader.load('...')

Or in R:

library(tacoreader)
dataset <- tacoreader::load('...')

🛰️ Sensor Information

Details about the sensors used for data collection

sentinel2msi

🎯 Task

The task associated with this dataset

semantic-segmentation

📂 Original Data Repository

Source location or archive of the raw data.

Raw Data Repository: https://huggingface.co/datasets/isp-uv-es/CloudSEN12Plus

💬 Discussion

Insights, commentary, or clarifications about the dataset.

Dataset Discussion: https://huggingface.co/datasets/tacofoundation/cloudsen12/discussions

🔀 Split Strategy

How the dataset is divided for training, validation, and testing.

Split Strategy: stratified

📚 Scientific Publications

Publications that reference or describe the dataset.

Publication 01

DOI: 10.1038/s41597-022-01878-2
Summary: CloudSEN12 first release. Only 509 x 509 patches.
BibTeX Citation:

@article{aybar2022cloudsen12,
  title={CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2},
  author={Aybar, Cesar and Ysuhuaylas, Luis and Loja, Jhomira and Gonzales, Karen and Herrera, Fernando and Bautista, Lesly and Yali, Roy and Flores, Angie and Diaz, Lissette and Cuenca, Nicole and others},
  journal={Scientific Data},
  volume={9},
  number={1},
  pages={782},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

Publication 02

DOI: 10.1109/IGARSS52108.2023.10282381
Summary: Exploration of incorrect annotations in cloud semantic segmentation datasets.
BibTeX Citation:

@inproceedings{aybar2023lessons,
  title={Lessons Learned From Cloudsen12 Dataset: Identifying Incorrect Annotations in Cloud Semantic Segmentation Datasets},
  author={Aybar, Cesar and Montero, David and Mateo-Garc{'\i}a, Gonzalo and G{'o}mez-Chova, Luis},
  booktitle={IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium},
  pages={892--895},
  year={2023},
  organization={IEEE}
}

Publication 03

DOI: 10.1016/j.dib.2024.110852
Summary: Extended version of CloudSEN12. We include 2000 x 2000 patches to the dataset.
BibTeX Citation:

@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},
  volume={56},
  pages={110852},
  year={2024},
  publisher={Elsevier}
}

🤝 Data Providers

Organizations or individuals responsible for the dataset.

Name	Role	URL
Image & Signal Processing	host	https://isp.uv.es/
European Space Agency (ESA)	producer	https://www.esa.int/

🧑‍🔬 Curators

Experts responsible for structuring the dataset in the TACO format.

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

🏷️ Labels

Annotated or classified data used for training models.

Name	Category	Description
clear	0	Pixels without cloud and cloud shadow contamination. They are primarily identified using bands B4- B3-B2, B1-B12-B13, and the cirrus band.
thick cloud	1	Opaque clouds that block all the reflected light from the Earth's surface. We identify them by assuming clouds exhibit distinctive shapes and maintain higher reflectance values in bands B4-B3-B2, B1-B12-B13, and the cirrus band.
thin cloud	2	Semitransparent clouds that alter the surface spectral signal but still allow to recognize the background. This is the hardest class to identify. We utilize CloudApp [1] to better understand the background, both with and without cloud cover.
cloud shadow	3	Dark pixels where light is occluded by thick or thin clouds. Cloud shadows depend on clouds presence and, by considering the solar position, we can identify and map these shadows through a reasoned projection of the cloud shape.

🌈 Optical Bands

Spectral bands used for data collection.

Name	Common Name	Description	Center Wavelength	Full Width Half Max	Index
B04	red	Band 4 - Red - 10m	664.5	29.0	3
B03	green	Band 3 - Green - 10m	560.0	34.0	2
B02	blue	Band 2 - Blue - 10m	496.5	53.0	1