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 '<em>raw'</em> and '<em>ready-to-train'-</em>versions with corresponding test datasets<em>. </em>The datasets each consist of the same 493 training and 20 test (without label data) scenes. The ‘<em>ready-to-train’</em>-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 original '<em>raw'</em>-version<em>. </em>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 '<em>ready-to-train</em>' 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

AI4Arctic海冰挑战赛数据集是为欧洲空间局（European Space Agency, ESA）ɸ实验室发起的AI4EO海冰竞赛制作的。该竞赛旨在开发深度学习模型，自动生成包含海冰浓度、发育阶段及浮冰大小（形态）信息的海冰图。训练数据集包含Sentinel-1主动微波合成孔径雷达（Synthetic Aperture Radar, SAR）数据，以及来自AMSR2卫星传感器的对应被动微波辐射计（MicroWave Radiometer, MWR）数据。尽管SAR数据在开阔水域与海冰之间存在歧义，但其空间分辨率较高；而MWR数据在开阔水域与冰之间具有良好的对比度，不过AMSR2 MWR观测的粗分辨率带来了新的障碍（如陆地溢出），这可能导致靠近开阔水域的海岸线附近海冰预测出现误差。挑战赛数据集的标签数据是丹麦气象研究所（Danish Meteorological Institute, DMI）的格陵兰冰服务部门及加拿大冰服务（Canadian Ice Service, CIS）为航行安全制作的海冰图。挑战赛数据集还包含其他辅助数据，如到陆地的距离和数值天气预报模型数据。场景时间范围为2018年1月8日至2021年12月21日。数据集分为“raw”和“ready-to-train”两个版本，各版本均配有对应的测试数据集。每个数据集包含493个训练场景和20个无标签测试场景。“ready-to-train”版本经过进一步预处理，包括将数据从40米像素间距下采样至80米、标准化缩放、转换海冰图（含浓度、发育阶段及浮冰大小信息）、去除nan值、掩码对齐等。本数据集为原始“raw”版本。netCDF文件按每组约25个打包，文件名格式对应获取SAR图像的Sentinel-1卫星（A/B），后跟首个文件获取时间至最后一个文件获取时间，即S1(A/B)_FirstDate_LastDate.zip。更多细节详见随数据集发布的通用手册《AI4Arctic_challenge-dataset-manual》。“ready-to-train”数据集的入门工具包代码可访问：https://github.com/astokholm/AI4ArcticSeaIceChallenge；挑战赛快速概述视频可查看：https://youtu.be/iuXIeLPyKfg。本项数据属于集合：https://doi.org/10.11583/DTU.c.6244065。