{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T16:55:04Z","timestamp":1781110504690,"version":"3.54.1"},"reference-count":49,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:00:00Z","timestamp":1614902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science and Innovation","doi-asserted-by":"publisher","award":["TEC2016-77741-R and PID2019-109026RB-I00, ERD"],"award-info":[{"award-number":["TEC2016-77741-R and PID2019-109026RB-I00, ERD"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The systematic monitoring of the Earth using optical satellites is limited by the presence of clouds. Accurately detecting these clouds is necessary to exploit satellite image archives in remote sensing applications. Despite many developments, cloud detection remains an unsolved problem with room for improvement, especially over bright surfaces and thin clouds. Recently, advances in cloud masking using deep learning have shown significant boosts in cloud detection accuracy. However, these works are validated in heterogeneous manners, and the comparison with operational threshold-based schemes is not consistent among many of them. In this work, we systematically compare deep learning models trained on Landsat-8 images on different Landsat-8 and Sentinel-2 publicly available datasets. Overall, we show that deep learning models exhibit a high detection accuracy when trained and tested on independent images from the same Landsat-8 dataset (intra-dataset validation), outperforming operational algorithms. However, the performance of deep learning models is similar to operational threshold-based ones when they are tested on different datasets of Landsat-8 images (inter-dataset validation) or datasets from a different sensor with similar radiometric characteristics such as Sentinel-2 (cross-sensor validation). The results suggest that (i) the development of cloud detection methods for new satellites can be based on deep learning models trained on data from similar sensors and (ii) there is a strong dependence of deep learning models on the dataset used for training and testing, which highlights the necessity of standardized datasets and procedures for benchmarking cloud detection models in the future.<\/jats:p>","DOI":"10.3390\/rs13050992","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T11:46:09Z","timestamp":1614944769000},"page":"992","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Benchmarking Deep Learning Models for Cloud Detection in Landsat-8 and Sentinel-2 Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4442-2507","authenticated-orcid":false,"given":"Dan","family":"L\u00f3pez-Puigdollers","sequence":"first","affiliation":[{"name":"Image Processing Laboratory, University of Valencia, 46980 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0569-393X","authenticated-orcid":false,"given":"Gonzalo","family":"Mateo-Garc\u00eda","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory, University of Valencia, 46980 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3924-1269","authenticated-orcid":false,"given":"Luis","family":"G\u00f3mez-Chova","sequence":"additional","affiliation":[{"name":"Image Processing Laboratory, University of Valencia, 46980 Valencia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1088\/1748-9326\/ab68ac","article-title":"Estimating and understanding crop yields with explainable deep learning in the Indian Wheat Belt","volume":"15","author":"Wolanin","year":"2020","journal-title":"Environ. Res. Lett."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.rse.2006.06.004","article-title":"Retrieval of oceanic chlorophyll concentration with relevance vector machines","volume":"105","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_3","unstructured":"Mateo-Garcia, G., Oprea, S., Smith, L., Veitch-Michaelis, J., Schumann, G., Gal, Y., Baydin, A.G., and Backes, D. (2019, January 8\u201314). Flood Detection On Low Cost Orbital Hardware. Proceedings of the Artificial Intelligence for Humanitarian Assistance and Disaster Response Workshop, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), Vancouver, BC, Canada."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.patrec.2005.08.004","article-title":"Urban Monitoring using Multitemporal SAR and Multispectral Data","volume":"27","author":"Calpe","year":"2006","journal-title":"Pattern Recognit. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4105","DOI":"10.1109\/TGRS.2007.905312","article-title":"Cloud-Screening Algorithm for ENVISAT\/MERIS Multispectral Images","volume":"45","author":"Calpe","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.rse.2014.12.014","article-title":"Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4\u20137, 8, and Sentinel 2 images","volume":"159","author":"Zhu","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_7","unstructured":"Louis, J., Debaecker, V., Pflug, B., Main-Knorn, M., Bieniarz, J., Mueller-Wilm, U., Cadau, E., and Gascon, F. (2016, January 9\u201313). Sentinel-2 sen2cor: L2a processor for users. Proceedings of the Living Planet Symposium, Prague, Czech Republic."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.isprsjprs.2019.02.017","article-title":"Deep learning based cloud detection for medium and high resolution remote sensing images of different sensors","volume":"150","author":"Li","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.isprsjprs.2019.08.018","article-title":"Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery","volume":"157","author":"Shendryk","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.rse.2019.03.039","article-title":"A cloud detection algorithm for satellite imagery based on deep learning","volume":"229","author":"Jeppesen","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1016\/j.rse.2019.03.007","article-title":"Cloud and cloud shadow detection in Landsat imagery based on deep convolutional neural networks","volume":"225","author":"Chai","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6195","DOI":"10.1109\/TGRS.2019.2904868","article-title":"CDnet: CNN-Based Cloud Detection for Remote Sensing Imagery","volume":"57","author":"Yang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","first-page":"100417","article-title":"CloudX-net: A robust encoder-decoder architecture for cloud detection from satellite remote sensing images","volume":"20","author":"Kanu","year":"2020","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1007\/s11082-020-02500-8","article-title":"Lightweight U-Net for cloud detection of visible and thermal infrared remote sensing images","volume":"52","author":"Zhang","year":"2020","journal-title":"Opt. Quantum Electron."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2019.11.024","article-title":"Transferring deep learning models for cloud detection between Landsat-8 and Proba-V","volume":"160","author":"Laparra","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Doxani, G., Vermote, E., Roger, J.C., Gascon, F., Adriaensen, S., Frantz, D., Hagolle, O., Hollstein, A., Kirches, G., and Li, F. (2018). Atmospheric Correction Inter-Comparison Exercise. Remote Sens., 10.","DOI":"10.3390\/rs10020352"},{"key":"ref_17","unstructured":"ESA (2020, January 28). CEOS-WGCV ACIX II\u2014CMIX: Atmospheric Correction Inter-Comparison Exercise\u2014Cloud Masking Inter-Comparison Exercise. Available online: https:\/\/earth.esa.int\/web\/sppa\/meetings-workshops\/hosted-and-co-sponsored-meetings\/acix-ii-cmix-2nd-ws."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4907","DOI":"10.3390\/rs6064907","article-title":"Automated Detection of Cloud and Cloud Shadow in Single-Date Landsat Imagery Using Neural Networks and Spatial Post-Processing","volume":"6","author":"Hughes","year":"2014","journal-title":"Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mohajerani, S., and Saeedi, P. (August, January 28). Cloud-Net: An end-to-end cloud detection algorithm for Landsat 8 imagery. Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898776"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Hollstein, A., Segl, K., Guanter, L., Brell, M., and Enesco, M. (2016). Ready-to-use methods for the detection of clouds, cirrus, snow, shadow, water and clear sky pixels in Sentinel-2 MSI images. Remote Sens., 8.","DOI":"10.3390\/rs8080666"},{"key":"ref_22","unstructured":"Baetens, L., and Olivier, H. (2019, February 19). Sentinel-2 Reference Cloud Masks Generated by an Active Learning Method. Available online: https:\/\/zenodo.org\/record\/1460961."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"100010","DOI":"10.1016\/j.srs.2020.100010","article-title":"Comparison of cloud detection algorithms for Sentinel-2 imagery","volume":"2","author":"Tarrio","year":"2020","journal-title":"Sci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zekoll, V., Main-Knorn, M., Louis, J., Frantz, D., Richter, R., and Pflug, B. (2021). Comparison of Masking Algorithms for Sentinel-2 Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13010137"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.rse.2011.10.028","article-title":"Object-based cloud and cloud shadow detection in Landsat imagery","volume":"118","author":"Zhu","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.rse.2018.04.046","article-title":"Improvement of the Fmask algorithm for Sentinel-2 images: Separating clouds from bright surfaces based on parallax effects","volume":"215","author":"Frantz","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111205","DOI":"10.1016\/j.rse.2019.05.024","article-title":"Fmask 4.0: Improved cloud and cloud shadow detection in Landsats 4\u20138 and Sentinel-2 imagery","volume":"231","author":"Qiu","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_28","unstructured":"Sentinel Hub Team (2020, January 28). Sentinel Hub\u2019s Cloud Detector for Sentinel-2 Imagery. Available online: https:\/\/github.com\/sentinel-hub\/sentinel2-cloud-detector."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1214\/aos\/1013203451","article-title":"Greedy function approximation: A gradient boosting machine","volume":"29","author":"Friedman","year":"2001","journal-title":"Ann. Statist."},{"key":"ref_30","unstructured":"Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., and Garnett, R. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems 30, Curran Associates, Inc."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1747","DOI":"10.1016\/j.rse.2010.03.002","article-title":"A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENuS, LANDSAT and SENTINEL-2 images","volume":"114","author":"Hagolle","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_32","unstructured":"Hollstein, A., Segl, K., Guanter, L., Brell, M., and Enesco, M. (2020, January 28). Database File of Manually Classified Sentinel-2A Data. Available online: https:\/\/gitext.gfz-potsdam.de\/EnMAP\/sentinel2_manual_classification_clouds\/blob\/master\/20170710_s2_manual_classification_data.h5."},{"key":"ref_33","unstructured":"U.S. Geological Survey (2016). L8 Biome Cloud Validation Masks, U.S. Geological Survey. U.S. Geological Survey Data Release."},{"key":"ref_34","unstructured":"U.S. Geological Survey (2016). L8 SPARCS Cloud Validation Masks, U.S. Geological Survey. U.S. Geological Survey Data Release."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mohajerani, S., Krammer, T.A., and Saeedi, P. (2018, January 29\u201331). A Cloud Detection Algorithm for Remote Sensing Images Using Fully Convolutional Neural Networks. Proceedings of the 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), Vancouver, BC, Canada.","DOI":"10.1109\/MMSP.2018.8547095"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Baetens, L., Desjardins, C., and Hagolle, O. (2019). Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. Remote Sens., 11.","DOI":"10.3390\/rs11040433"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., Krishnan, D., Taylor, G.W., and Fergus, R. (2010, January 13\u201318). Deconvolutional networks. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539957"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2015, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111203","DOI":"10.1016\/j.rse.2019.05.022","article-title":"Multi-sensor cloud and cloud shadow segmentation with a convolutional neural network","volume":"230","author":"Wieland","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Hughes, M.J., and Kennedy, R. (2019). High-Quality Cloud Masking of Landsat 8 Imagery Using Convolutional Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11212591"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.14358\/PERS.72.10.1179","article-title":"Characterization of the Landsat-7 ETM+ Automated Cloud-Cover Assessment (ACCA) Algorithm","volume":"72","author":"Irish","year":"2006","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Raiyani, K., Gon\u00e7alves, T., Rato, L., Salgueiro, P., and Marques da Silva, J.R. (2021). Sentinel-2 Image Scene Classification: A Comparison between Sen2Cor and a Machine Learning Approach. Remote Sens., 13.","DOI":"10.3390\/rs13020300"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-020-18321-y","article-title":"Spatial validation reveals poor predictive performance of large-scale ecological mapping models","volume":"11","author":"Ploton","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1109\/JSTARS.2020.3031741","article-title":"Cross-Sensor Adversarial Domain Adaptation of Landsat-8 and Proba-V Images for Cloud Detection","volume":"14","author":"Laparra","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","unstructured":"Kingma, D.P., and Ba, J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations, (ICLR 2015), San Diego, CA, USA."},{"key":"ref_48","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2021, February 03). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: tensorflow.org."},{"key":"ref_49","unstructured":"USGS (2020, January 28). Comparison of Sentinel-2 and Landsat, Available online: http:\/\/www.usgs.gov\/centers\/eros\/science\/usgs-eros-archive-sentinel-2-comparison-sentinel-2-and-landsat."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/992\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:33:31Z","timestamp":1760160811000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/5\/992"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,5]]},"references-count":49,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13050992"],"URL":"https:\/\/doi.org\/10.3390\/rs13050992","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,5]]}}}