{"url":"/dataset/proba-v","name":"PROBA-V","full_name":"PROBA-V Super-Resolution dataset","description_markdown":"The PROBA-V Super-Resolution dataset is the official dataset of ESA's Kelvins competition for \"PROBA-V Super Resolution\". It contains satellite data from 74 hand-selected regions around the globe at different points in time. The data is composed of radiometrically and geometrically corrected Top-Of-Atmosphere (TOA) reflectances for the RED and NIR spectral bands at 300m and 100m resolution in Plate Carrée projection. The 300m resolution data is delivered as 128x128 grey-scale pixel images, the 100m resolution data as 384x384 grey-scale pixel images. Additionally, a quality map is provided for each pixel, indicating whether the pixels are concealed (i.e. by clouads, ice, water, missing information, etc.).\r\n\r\nThe goal of the challenge can be described as Multi-Image Super-resolution: Construct a single high-resolution image out of a series of more frequent low resolution images.\r\n\r\nDetailed information about the related competition can be found at https://kelvins.esa.int/proba-v-super-resolution.","description_withheld":null,"homepage":"https://kelvins.esa.int/proba-v-super-resolution/","introduced_date":"2018-10-15","introduced_date_note":null,"introduced_by":null,"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Super-Resolution","url":"/task/super-resolution","datasets_with_task":"/datasets/task/super-resolution"},{"name":"Image Super-Resolution","url":"/task/image-super-resolution","datasets_with_task":"/datasets/task/image-super-resolution"},{"name":"Multi-Frame Super-Resolution","url":"/task/multi-frame-super-resolution","datasets_with_task":"/datasets/task/multi-frame-super-resolution"},{"name":"satellite image super-resolution","url":"/task/satellite-image-super-resolution","datasets_with_task":"/datasets/task/satellite-image-super-resolution"}],"languages":[],"variants":["PROBA-V"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/multi-frame-super-resolution-on-proba-v","task":"Multi-Frame Super-Resolution","dataset_variant":"PROBA-V","rows":8,"metrics":["Normalized cPSNR"],"first_row_in_archive_order":{"model":"TR-MISR","paper":"/paper/tr-misr-multiimage-super-resolution-based-on","metrics":{"Normalized cPSNR":"0.9300466898779116"},"code_links":[{"title":"Suanmd/TR-MISR","url":"https://github.com/Suanmd/TR-MISR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/tr-misr-multiimage-super-resolution-based-on","title":"TR-MISR: Multiimage Super-Resolution Based on Feature Fusion With Transformers","date":"2022-02-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/permutation-invariance-and-uncertainty-in","title":"Permutation invariance and uncertainty in multitemporal image super-resolution","date":"2021-05-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":0,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-image-super-resolution-of-remotely","title":"Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks","date":"2020-07-06","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/highres-net-recursive-fusion-for-multi-frame","title":"HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery","date":"2020-02-15","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/highres-net-multi-frame-super-resolution-by","title":"HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion","date":"2020-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deepsum-deep-neural-network-for-super","title":"DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images","date":"2019-07-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/wide-activation-for-efficient-and-accurate","title":"Wide Activation for Efficient and Accurate Image Super-Resolution","date":"2018-08-27","rows_on_this_dataset":2,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":0,"samples_unverified":17,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":25,"samples_ran":0,"samples_unverified":25,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":2,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}