{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T15:57:24Z","timestamp":1784822244414,"version":"3.55.0"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CNES"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Data"],"abstract":"<jats:p>Boosted by the progress in deep learning, Single Image Super-Resolution (SISR) has gained a lot of interest in the remote sensing community, who sees it as an opportunity to compensate for satellites\u2019 ever-limited spatial resolution with respect to end users\u2019 needs. This is especially true for Sentinel-2 because of its unique combination of resolution, revisit time, global coverage and free and open data policy. While there has been a great amount of work on network architectures in recent years, deep-learning-based SISR in remote sensing is still limited by the availability of the large training sets it requires. The lack of publicly available large datasets with the required variability in terms of landscapes and seasons pushes researchers to simulate their own datasets by means of downsampling. This may impair the applicability of the trained model on real-world data at the target input resolution. This paper presents SEN2VEN\u00b5S, an open-data licensed dataset composed of 10 m and 20 m cloud-free surface reflectance patches from Sentinel-2, with their reference spatially registered surface reflectance patches at 5 m resolution acquired on the same day by the VEN\u00b5S satellite. This dataset covers 29 locations on earth with a total of 132,955 patches of 256 \u00d7 256 pixels at 5 m resolution and can be used for the training and comparison of super-resolution algorithms to bring the spatial resolution of 8 of the Sentinel-2 bands up to 5 m.<\/jats:p>","DOI":"10.3390\/data7070096","type":"journal-article","created":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T22:06:00Z","timestamp":1657749960000},"page":"96","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["SEN2VEN\u00b5S, a Dataset for the Training of Sentinel-2 Super-Resolution Algorithms"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7436-0381","authenticated-orcid":false,"given":"Julien","family":"Michel","sequence":"first","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES, CNRS, INRAE, IRD, UT3, 18 Avenue Edouard Belin BPI 2801, CEDEX 9, 31401 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Vinasco-Salinas","sequence":"additional","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES, CNRS, INRAE, IRD, UT3, 18 Avenue Edouard Belin BPI 2801, CEDEX 9, 31401 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6896-0049","authenticated-orcid":false,"given":"Jordi","family":"Inglada","sequence":"additional","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES, CNRS, INRAE, IRD, UT3, 18 Avenue Edouard Belin BPI 2801, CEDEX 9, 31401 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2358-0493","authenticated-orcid":false,"given":"Olivier","family":"Hagolle","sequence":"additional","affiliation":[{"name":"CESBIO, Universit\u00e9 de Toulouse, CNES, CNRS, INRAE, IRD, UT3, 18 Avenue Edouard Belin BPI 2801, CEDEX 9, 31401 Toulouse, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lanaras, C., Bioucas-Dias, J., Baltsavias, E., and Schindler, K. (2017, January 21\u201326). Super-resolution of multispectral multiresolution images from a single sensor. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.194"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Paris, C., Bioucas-Dias, J., and Bruzzone, L. (2017, January 23\u201328). A hierarchical approach to superresolution of multispectral images with different spatial resolutions. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127525"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3352","DOI":"10.1109\/TGRS.2019.2953808","article-title":"An explicit and scene-adapted definition of convex self-similarity prior with application to unsupervised Sentinel-2 super-resolution","volume":"58","author":"Lin","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Gargiulo, M., Mazza, A., Gaetano, R., Ruello, G., and Scarpa, G. (2019). Fast super-resolution of 20 m Sentinel-2 bands using convolutional neural networks. Remote Sens., 11.","DOI":"10.3390\/rs11222635"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.isprsjprs.2018.09.018","article-title":"Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network","volume":"146","author":"Lanaras","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Palsson, F., Sveinsson, J.R., and Ulfarsson, M.O. (2018). Sentinel-2 Image Fusion Using a Deep Residual Network. Remote Sens., 10.","DOI":"10.3390\/rs10081290"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6882","DOI":"10.1109\/JSTARS.2021.3092286","article-title":"Sentinel-2 sharpening using a single unsupervised convolutional neural network with MTF-based degradation model","volume":"14","author":"Nguyen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ciotola, M., Ragosta, M., Poggi, G., and Scarpa, G. (2021, January 11\u201316). A full-resolution training framework for Sentinel-2 image fusion. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9553199"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1109\/83.661187","article-title":"Total variation blind deconvolution","volume":"7","author":"Chan","year":"1998","journal-title":"IEEE Trans. Image Process."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Krishnan, D., Tay, T., and Fergus, R. (2011, January 20\u201325). Blind deconvolution using a normalized sparsity measure. Proceedings of the CVPR 2011, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995521"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3390462","article-title":"A deep journey into super-resolution: A survey","volume":"53","author":"Anwar","year":"2020","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"100901","DOI":"10.1117\/1.OE.60.10.100901","article-title":"Research on super-resolution reconstruction of remote sensing images: A comprehensive review","volume":"60","author":"Liu","year":"2021","journal-title":"Opt. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Agustsson, E., and Timofte, R. (2017, January 21\u201326). Ntire 2017 challenge on single image super-resolution: Dataset and study. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Shoeiby, M., Robles-Kelly, A., Wei, R., and Timofte, R. (2018, January 8\u201314). Pirm2018 challenge on spectral image super-resolution: Dataset and study. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_18"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wang, L., Yang, J., An, W., and Guo, Y. (2019, January 16\u201317). Flickr1024: A large-scale dataset for stereo image super-resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshops, Long Beach, CA, USA.","DOI":"10.1109\/ICCVW.2019.00478"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1007\/s00371-020-01957-8","article-title":"Paradigm shifts in super-resolution techniques for remote sensing applications","volume":"37","author":"Rohith","year":"2021","journal-title":"Vis. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Pouliot, D., Latifovic, R., Pasher, J., and Duffe, J. (2018). Landsat super-resolution enhancement using convolution neural networks and Sentinel-2 for training. Remote Sens., 10.","DOI":"10.3390\/rs10030394"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"95","DOI":"10.5194\/isprs-archives-XLII-2-W16-95-2019","article-title":"Super-Resolution for Sentinel-2 Images","volume":"XLII-2\/W16","author":"Galar","year":"2019","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Salgueiro Romero, L., Marcello, J., and Vilaplana, V. (2020). Super-resolution of sentinel-2 imagery using generative adversarial networks. Remote Sens., 12.","DOI":"10.3390\/rs12152424"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5194\/isprs-archives-XLIII-B1-2020-9-2020","article-title":"A generative adversarial network approach for super-resolution of sentinel-2 satellite images","volume":"43","author":"Pineda","year":"2020","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tao, Y., Xiong, S., Song, R., and Muller, J.P. (2021). Towards Streamlined Single-Image Super-Resolution: Demonstration with 10 m Sentinel-2 Colour and 10\u201360 m Multi-Spectral VNIR and SWIR Bands. Remote Sens., 13.","DOI":"10.3390\/rs13132614"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Galar, M., Sesma, R., Ayala, C., Albizua, L., and Aranda, C. (2020). Super-resolution of sentinel-2 images using convolutional neural networks and real ground truth data. Remote Sens., 12.","DOI":"10.3390\/rs12182941"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/s42064-019-0059-8","article-title":"Super-resolution of PROBA-V images using convolutional neural networks","volume":"3","author":"Izzo","year":"2019","journal-title":"Astrodynamics"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V.R., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 data for land cover\/use mapping: A review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Segarra, J., Buchaillot, M.L., Araus, J.L., and Kefauver, S.C. (2020). Remote sensing for precision agriculture: Sentinel-2 improved features and applications. Agronomy, 10.","DOI":"10.3390\/agronomy10050641"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Misra, G., Cawkwell, F., and Wingler, A. (2020). Status of phenological research using Sentinel-2 data: A review. Remote Sens., 12.","DOI":"10.3390\/rs12172760"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ferrier, P., Crebassol, P., Dedieu, G., Hagolle, O., Meygret, A., Tinto, F., Yaniv, Y., and Herscovitz, J. (2010, January 25\u201330). VEN\u03bcS (Vegetation and environment monitoring on a new micro satellite). Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5652087"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Dedieu, G., Hagolle, O., Karnieli, A., Ferrier, P., Cr\u00e9bassol, P., Gamet, P., Desjardins, C., Yakov, M., Cohen, M., and Hayun, E. (2018, January 22\u201327). VEN\u00b5S: Performances and First Results after 11 Months in Orbit. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8519207"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1000107","DOI":"10.1117\/12.2240935","article-title":"Maccs-atcor joint algorithm (maja)","volume":"Volume 10001","author":"Lonjou","year":"2016","journal-title":"Proceedings of the Remote Sensing of Clouds and the Atmosphere XXI"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Buchhorn, M., Smets, B., Bertels, L., Roo, B.D., Lesiv, M., Tsendbazar, N.E., Herold, M., and Fritz, S. (2022, May 12). Copernicus Global Land Service: Land Cover 100m: Collection 3: Epoch 2019: Globe. Available online: https:\/\/doi.org\/10.5281\/zenodo.3939050.","DOI":"10.3390\/rs12061044"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Dick, A., Raynaud, J.L., Rolland, A., Pelou, S., Coustance, S., Dedieu, G., Hagolle, O., Burochin, J.P., Binet, R., and Moreau, A. (2022). VEN\u03bcS: Mission Characteristics, Final Evaluation of the First Phase and Data Production. Remote Sens., 14.","DOI":"10.3390\/rs14143281"},{"key":"ref_33","first-page":"2","article-title":"Sift-the scale invariant feature transform","volume":"2","author":"Lowe","year":"2004","journal-title":"Int. J."},{"key":"ref_34","first-page":"171","article-title":"A new satellite imagery stereo pipeline designed for scalability, robustness and performance","volume":"2","author":"Michel","year":"2020","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"265","DOI":"10.5194\/isprs-archives-XLIII-B3-2021-265-2021","article-title":"Learning Harmonised Pleiades and SENTINEL-2 Surface Reflectances","volume":"43","author":"Michel","year":"2021","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_36","unstructured":"Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., and Garnett, R. (2019). PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems 32, Curran Associates, Inc."},{"key":"ref_37","unstructured":"Michel, J., Hagolle, O., Puissant, A., Herrault, P.A., Corpetti, T., Nabucet, J., Faure, J.F., Maurel, P., Lelong, C., and Berthier, E. (2022). Sentinel-HR Phase 0 Report, CESBIO. CNES-Centre National d\u2019\u00e9tudes Spatiales."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., and Sohn, K.A. (2018, January 8\u201314). Fast, accurate, and lightweight super-resolution with cascading residual network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_16"}],"updated-by":[{"DOI":"10.3390\/data8030051","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2022,7,13]],"date-time":"2022-07-13T00:00:00Z","timestamp":1657670400000}}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/7\/7\/96\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,3]],"date-time":"2025-08-03T14:36:21Z","timestamp":1754231781000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/7\/7\/96"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,13]]},"references-count":38,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,7]]}},"alternative-id":["data7070096"],"URL":"https:\/\/doi.org\/10.3390\/data7070096","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202205.0230.v1","asserted-by":"object"}]},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,13]]}}}