{"url":"/dataset/magicbathynet","name":"MagicBathyNet","full_name":null,"description_markdown":"MagicBathyNet is a benchmark dataset made up of image patches of Sentinel-2, SPOT-6 and aerial imagery, bathymetry in raster format and seabed classes annotations. Dataset also facilitates unsupervised learning for model pre-training in shallow coastal areas. \r\n\r\nMagicBathyNet contains 3355 RGB co-registered triplets of Sentinel-2 (S2), SPOT-6, and aerial image patches, complemented by 1244 RGB co-registered S2 and SPOT-6 doublets, 3354 DSM (Digital Surface Model) raster patches for the aerial patches and 3396 DSM raster patches for S2 and SPOT-6. Additionally, it contains 533 annotated raster patches for seabed habitat and type, facilitating supervised pixel-based classification. Each patch covers 180x180m, represented by 18x18 pixels in S2 imagery, 30x30 pixels in SPOT-6 imagery and 720x720 pixels in airborne imagery.","description_withheld":null,"homepage":"https://www.magicbathy.eu/magicbathynet.html","introduced_date":"2024-05-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/magicbathynet-a-multimodal-remote-sensing","title":"MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters","first_author":"Panagiotis Agrafiotis","url":null},"license":{"name":"Creative Commons Attribution Non Commercial 4.0 International","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"LiDAR","url":"/datasets/modality/lidar"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Depth Prediction","url":"/task/depth-prediction","datasets_with_task":"/datasets/task/depth-prediction"},{"name":"Bathymetry prediction","url":"/task/bathymetry-prediction","datasets_with_task":"/datasets/task/bathymetry-prediction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["MagicBathyNet"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}