Ready-To-Train 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 493 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 Test data for the Ready-To-Train version. Reference data is not 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.6244065AI4Arctic海冰挑战赛数据集是专为欧洲空间局（European Space Agency，ESA）ɸ实验室发起的AI4EO海冰竞赛打造的。该竞赛旨在研发深度学习模型，以自动生成包含海冰密集度、发展阶段以及浮冰尺寸（形态）信息的海冰冰情图。 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卫星传感器的被动微波辐射计（MicroWave Radiometer，MWR）配套数据。主动微波SAR数据虽存在开阔水域与海冰的识别歧义，但空间分辨率较高；而MWR数据可较好区分开阔水域与海冰，不过AMSR2 MWR观测的粗分辨率带来了一系列新的挑战，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。 本次挑战赛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）与加拿大冰情服务中心（Canadian Ice Service，CIS）为保障航行安全制作。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。数据集的场景采集时间范围为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“原始版”与“就绪训练版”，并配套对应测试数据集。两个版本均包含完全相同的493个训练场景与20个无标签测试场景。其中“就绪训练版”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米降采样、标准化处理、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）、移除缺失值、掩膜对齐等操作。 此处为就绪训练版的测试数据，未包含参考数据。更多详细信息可参见随数据集一同发布的通用手册《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。该挑战赛的快速视频概述可通过以下链接查看：https://youtu.be/iuXIeLPyKfg。本数据集隶属于以下集合：https://doi.org/10.11583/DTU.c.6244065