Raw AI4Arctic Sea Ice Challenge Test 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 513 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 testing data for the 'raw'-version. No reference data is included. 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

AI4Arctic海冰挑战赛数据集（AI4Arctic Sea Ice Challenge Datasets）是为欧洲空间局（European Space Agency, ESA）ɸ-lab发起的AI4EO海冰竞赛打造的专属数据集。本次竞赛的目标是研发深度学习模型，以自动生成海冰冰情图，涵盖海冰密集度、冰型发展阶段以及浮冰尺寸（形态）三类核心信息。训练数据集包含哨兵一号主动微波合成孔径雷达（Sentinel-1 active microwave Synthetic Aperture Radar, SAR）数据，以及来自AMSR2卫星传感器的配套被动微波辐射计（passive MicroWave Radiometer, MWR）数据。尽管合成孔径雷达数据在开阔水域与海冰的区分上存在歧义，但其空间分辨率较高；而被动微波辐射计数据在开阔水域与海冰间具备良好的对比度。但AMSR2被动微波辐射计观测数据的粗分辨率带来了一系列新的难题，例如陆地溢渗效应（land spill-over），该效应可能导致紧邻开阔水域的海岸线区域出现海冰预测误差。本挑战赛数据集的标注数据为海冰冰情图，由丹麦气象研究所（Danish Meteorological Institute, DMI）下属的格陵兰冰情服务部门以及加拿大冰情服务处（Canadian Ice Service, CIS）为保障航行安全制作而成。该挑战赛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。数据集覆盖的场景采集时段为2018年1月8日至2021年12月21日。该数据集包含两个版本，即"原始版（raw）"与"就绪训练版（ready-to-train）"，且各配有对应的测试数据集。两个版本的数据集均包含513组训练场景与20组无标注的测试场景。"就绪训练版"已针对模型训练做了进一步预处理，包括将数据下采样至40至80米的像素间距、标准化处理、转换冰情图格式（涵盖海冰密集度、冰型发展阶段与浮冰尺寸）、剔除非数值（NaN）值以及掩膜对齐等操作。此为"原始版"的测试数据，未包含参考数据。更多细节可查阅随数据集一同发布的官方手册《AI4Arctic_challenge-dataset-manual》。针对"就绪训练版"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概览可通过以下链接观看：https://youtu.be/iuXIeLPyKfg。本数据集隶属于集合数据集https://doi.org/10.11583/DTU.c.6244065。