Ready-To-Train 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 Ready-To-Train version. 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海冰挑战数据集是为欧洲空间局（European Space Agency, ESA）ɸ-lab发起的AI4EO海冰竞赛所制作。本次竞赛的目标是开发深度学习模型，以自动生成涵盖海冰密集度、发展阶段及浮冰尺寸（形态）信息的海冰冰情图。

训练数据集包含哨兵-1（Sentinel-1）主动微波合成孔径雷达（Synthetic Aperture Radar, SAR）数据，以及搭载于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在开阔水域与海冰之间存在识别歧义，但其空间分辨率极高；而MWR数据在开阔水域与海冰间的对比度表现优异，但AMSR2 MWR观测的粗分辨率带来了一系列新的问题，例如陆地溢漏效应，这会导致紧邻开阔水域的海岸线周边出现错误的海冰预测结果。

本次挑战数据集的标签数据为海冰冰情图，由丹麦气象研究所（Danish Meteorological Institute, DMI）下属格陵兰冰情服务部门与加拿大冰情服务中心（Canadian Ice Service, CIS）为保障航海安全制作。

挑战数据集还包含其他辅助数据，例如距陆地距离数据与数值天气预报模式数据。所有场景的时间跨度为2018年1月8日至2021年12月21日。

该数据集共有两个版本，分别为"raw（原始）"版本与"ready-to-train（可直接用于训练）"版本，且均配有对应的测试数据集。两个版本的数据集均包含完全一致的493个训练场景与20个无标签测试场景。其中"ready-to-train（可直接用于训练）"版本已针对模型训练完成了预处理工作，包括将像素间距从40至80米进行下采样、标准化缩放、转换海冰冰情图格式（涵盖海冰密集度、发展阶段与浮冰尺寸）、去除NaN值、掩膜对齐等。当前提供的即为该可直接用于训练的版本。

更多详细信息可查阅随数据集一同发布的通用手册《AI4Arctic_challenge-dataset-manual》。针对"ready-to-train"数据集的入门工具包代码地址为：https://github.com/astokholm/AI4ArcticSeaIceChallenge。竞赛快速视频概览地址为：https://youtu.be/iuXIeLPyKfg。本数据集隶属于集合https://doi.org/10.11583/DTU.c.6244065