five

MARIDA: Marine Debris Archive

收藏
NIAID Data Ecosystem2026-03-13 收录
下载链接:
https://zenodo.org/record/5151940
下载链接
链接失效反馈
官方服务:
资源简介:
MARIne Debris Archive (MARIDA) is a marine debris-oriented dataset on Sentinel-2 satellite images. It also includes various sea features that co-exist. MARIDA is primarily focused on the weakly supervised pixel-level semantic segmentation task.  Citation: Kikaki K, Kakogeorgiou I, Mikeli P, Raitsos DE, Karantzalos K (2022) MARIDA: A benchmark for Marine Debris detection from Sentinel-2 remote sensing data. PLoS ONE 17(1): e0262247. https://doi.org/10.1371/journal.pone.0262247 For the quick start guide visit marine-debris.github.io   The dataset contains: i. 1381 patches (256 x 256) structured by Unique Dates and S2 Tiles. Each patch is provided along with the corresponding masks of pixel-level annotated classes (*_cl) and confidence levels (*_conf). Patches are given in GeoTiff format. ii. Shapefiles data in WGS’84/ UTM projection, with file naming convention following the scheme: s2_dd-mm-yy_ttt, where s2 denotes the S2 sensor, dd denotes the day, mm the month, yy the year and ttt denotes the S2 tile. Shapefiles include the class of each annotation along with the confidence level and the marine debris report description. iii. Train, Validation and Test split for evaluating machine learning algorithms. iv. The assigned multi-labels for each patch (labels_mapping.txt). The mapping between Digital Numbers and Classes is: 1: Marine Debris 2: Dense Sargassum 3: Sparse Sargassum 4: Natural Organic Material 5: Ship 6: Clouds 7: Marine Water 8: Sediment-Laden Water 9: Foam 10: Turbid Water 11: Shallow Water 12: Waves 13: Cloud Shadows 14: Wakes 15: Mixed Water The mapping between Digital Numbers and Confidence level is: 1: High 2: Moderate 3: Low The mapping between Digital Numbers and marine debris Report existence is: 1: Very close 2: Away 3: No   The final uncompressed dataset requires 4.38 GB of storage.
创建时间:
2022-01-23
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作