Raw AI4Arctic Sea Ice Challenge Dataset

The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the 'raw' and 'ready-to-train'-versions with corresponding test datasets. The datasets each consist of the same 512 training and 20 test (without label data) scenes. The ‘ready-to-train’-version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the 'raw'-version. The netCDF files are bundled together in groups ~25 with the filename format corresponding to the Sentinel-1 satellite from which the SAR image was acquired by, followed by the first file acquisition time to the last, i.e. S1(A/B)_FirstDate_LastDate.zip. Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”. Code with a get-started toolkit for the 'ready-to-train' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065 Version 2 has updated two zip files, which contained four corrupted netCDF files. The zip files in question are: S1A_20190419T203541_20190823T114541.zip S1B_20191028T132359_20200714T184241.zip In addition, 20 more scenes have been added in "added_v2.zip". Version 3 fixes an error with a duplicate zip file starting with "S1A_20190419T203541_".., adds the "S1A_20181018T121002_20190415T211043.zip" file and removed a scene with a faulty ice chart resulting in an updated "S1A_20191201T205227_20200619T122818.zip" file.AI4Arctic海冰挑战数据集（AI4Arctic Sea Ice Challenge Datasets）是为欧洲空间局（European Space Agency, ESA）ɸ-lab发起的AI4EO海冰竞赛所制作的数据集。本次竞赛的目标为研发深度学习模型，以自动生成海冰图（sea ice charts），涵盖海冰密集度、发展阶段以及浮冰尺寸（形态）信息。训练数据集包含哨兵1号（Sentinel-1）主动微波合成孔径雷达（Synthetic Aperture Radar, SAR）数据，以及来自AMSR2卫星传感器的被动微波辐射计（MicroWave Radiometer, MWR）配套观测数据。尽管SAR数据在开阔水域与海冰之间存在解译歧义，但其空间分辨率较高；而MWR数据则能较好地区分开阔水域与海冰。但AMSR2 MWR观测数据的分辨率较低，带来了一系列新的问题，例如陆地溢出效应，这可能会在毗邻开阔水域的海岸沿线引发错误的海冰预测结果。本挑战数据集的标签数据为海冰图，由丹麦气象研究所（Danish Meteorological Institute, DMI）下属的格陵兰冰务部门以及加拿大冰情服务中心（Canadian Ice Service, CIS）为保障航行安全所制作。本挑战数据集还包含其他辅助数据，例如距陆地距离数据以及数值天气预报模式数据。数据集覆盖的时间范围为2018年1月8日至2021年12月21日。本数据集包含两个版本："原始版"与"可训练版"，并配有对应的测试数据集。两个版本均包含完全相同的512个训练场景与20个无标签测试场景。"可训练版"已针对模型训练做了进一步预处理，包括将像素间距从40至80米下采样、标准化缩放、转换海冰图格式（涵盖海冰密集度、发展阶段与浮冰尺寸）、去除无效值（nan值）以及掩膜对齐等操作；原始版则未经过此类优化处理。网络通用数据格式（netCDF）文件以约25个为一组进行打包，文件名格式与获取SAR图像的哨兵1号卫星（Sentinel-1）对应，格式为`S1(A/B)_首景获取时间_末景获取时间.zip`，即`S1(A/B)_FirstDate_LastDate.zip`。数据集的更多详细说明可参阅随数据集一同发布的《AI4Arctic_challenge-dataset-manual》手册。针对"可训练版"数据集的入门工具包代码可访问：https://github.com/astokholm/AI4ArcticSeaIceChallenge。本次竞赛的快速视频介绍可访问：https://youtu.be/iuXIeLPyKfg。本数据集隶属于馆藏资源 https://doi.org/10.11583/DTU.c.6244065。V2版本更新了两个包含4个损坏netCDF文件的压缩包，涉及的压缩包为：`S1A_20190419T203541_20190823T114541.zip`与`S1B_20191028T132359_20200714T184241.zip`。此外，V2版本还在`added_v2.zip`中新增了20个场景数据。V3版本修复了一个以`S1A_20190419T203541_`开头的重复压缩包问题，新增了`S1A_20181018T121002_20190415T211043.zip`压缩包，并移除了一个存在错误海冰图的场景，最终更新了`S1A_20191201T205227_20200619T122818.zip`压缩包。