{"url":"/dataset/marida","name":"MARIDA","full_name":"Marine Debris Archive","description_markdown":"**MARIDA** (**Marine Debris Archive**) is the first dataset based on the multispectral Sentinel-2 (S2) satellite data, which distinguishes Marine Debris from various marine features that co-exist, including Sargassum macroalgae, Ships, Natural Organic Material, Waves, Wakes, Foam, dissimilar water types (i.e., Clear, Turbid Water, Sediment-Laden Water, Shallow Water), and Clouds. MARIDA is an open-access dataset which enables the research community to explore the spectral behaviour of certain floating materials, sea state features and water types, to develop and evaluate Marine Debris detection solutions based on artificial intelligence and deep learning architectures, as well as satellite pre-processing pipelines.  Although it is designed to be beneficial for several machine learning tasks, it primarily aims to benchmark weakly supervised pixel-level semantic segmentation learning methods. \r\n\r\nMARIDA can be downloaded from the repository Zenodo ([https://doi.org/10.5281/zenodo.5151941](https://zenodo.org/record/5151941#.YfFZ_PXP30o)). A quick start guide for all ML benchmarks and the detailed overview of the dataset are available at [https://marine-debris.github.io/](https://marine-debris.github.io/).","description_withheld":null,"homepage":"https://marine-debris.github.io/","introduced_date":"2022-01-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/marida-a-benchmark-for-marine-debris","title":"MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data","first_author":"Katerina Kikaki","url":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":"Image Segmentation","url":"/task/image-segmentation","datasets_with_task":"/datasets/task/image-segmentation"},{"name":"Weakly supervised segmentation","url":"/task/weakly-supervised-segmentation","datasets_with_task":"/datasets/task/weakly-supervised-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MARIDA"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-segmentation-on-marida","task":"Image Segmentation","dataset_variant":"MARIDA","rows":2,"metrics":["IoU","F1","F1@M"],"first_row_in_archive_order":{"model":"ResAttUNet","paper":"/paper/resattunet-detecting-marine-debris-using-an","metrics":{"F1@M":"0.95","IoU":"0.67"},"code_links":[{"title":"sheikhazhanmohammed/sadma","url":"https://github.com/sheikhazhanmohammed/sadma"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/resattunet-detecting-marine-debris-using-an","title":"ResAttUNet: Detecting Marine Debris using an Attention activated Residual UNet","date":"2022-10-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/marida-a-benchmark-for-marine-debris","title":"MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data","date":"2022-01-07","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}