CloudSEN12 trained models

This repository contains the trained models of the publications:

Aybar, C., Ysuhuaylas, L., Loja, J., Gonzales, K., Herrera, F., Bautista, L., Yali, R., Flores, A., Diaz, L., Cuenca, N., Espinoza, W., Prudencio, F., Llactayo, V., Montero, D., Sudmanns, M., Tiede, D., Mateo-García, G., & Gómez-Chova, L. (2022). CloudSEN12, a global dataset for semantic understanding of cloud and cloud shadow in Sentinel-2. Scientific Data, 9(1), Article 1. DOI: 10.1038/s41597-022-01878-2

Aybar, C., Montero, D., Mateo-García, G., & Gómez-Chova, L. (2023). Lessons Learned From Cloudsen12 Dataset: Identifying Incorrect Annotations in Cloud Semantic Segmentation Datasets. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 892–895. DOI: 10.1109/IGARSS52108.2023.10282381

Mateo-García, G., Aybar, C., Acciarini, G., Růžička, V., Meoni, G., Longépé, N., & Gómez-Chova, L. (2023). Onboard Cloud Detection and Atmospheric Correction with Deep Learning Emulators. IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, 1875–1878. DOI: 10.1109/IGARSS52108.2023.10282605

Aybar, C., et al (2024), CloudSEN12+: The largest dataset of expert-labeled pixels for cloud and cloud shadow detection in Sentinel-2 (Submitted)

We include the trained models:

cloudsen12 Model trained on the 13 bands of Sentinel-2 L1C in the CloudSEN12 dataset
cloudsen12l2a Model trained on the 12 bands of Sentinel-2 L2A in the CloudSEN12 dataset
dtacs4bands Model trained on the NIR, RED, GREEN and BLUE bands of Sentinel-2 L1C in the CloudSEN12 dataset
landsat30 Model trained on the common bands of Sentinel-2 L1C and Landsat 8 and 9 in the CloudSEN12 dataset

In order to run any of these models in a Sentinel-2 scene see the tutorial Run CloudSEN12 model in the cloudsen12_models package.

If you find this work useful please cite:

@article{aybar_cloudsen12_2022,
    title = {{CloudSEN12}, a global dataset for semantic understanding of cloud and cloud shadow in {Sentinel}-2},
    volume = {9},
    issn = {2052-4463},
    url = {https://www.nature.com/articles/s41597-022-01878-2},
    doi = {10.1038/s41597-022-01878-2},
    number = {1},
    urldate = {2023-01-02},
    journal = {Scientific Data},
    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 Espinoza, Wendy and Prudencio, Fernando and Llactayo, Valeria and Montero, David and Sudmanns, Martin and Tiede, Dirk and Mateo-García, Gonzalo and Gómez-Chova, Luis},
    month = dec,
    year = {2022},
    pages = {782},
}

Licence

All pre-trained models in this repository are released under a Creative Commons non-commercial licence

The cloudsen12_models python package is published under a GNU Lesser GPL v3 licence

Acknowledgments

This research has been supported by the DEEPCLOUD project (PID2019-109026RB-I00, University of Valencia) funded by the Spanish Ministry of Science and Innovation (MCIN/AEI/10.13039/501100011033) and the European Union (NextGenerationEU).