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 '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 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 '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 20 additional scenes and has been reprocessed to accommodate the updated mean and STandard Deviation (std). Furthermore, SOD and FLOE variables have been slightly altered from version 1, as the dominant ice code threshold was incorrectly set to 70% and 50%, respectively, instead of the 65%, which was otherwise specified in the dataset manual.
Version 3 removes a scene with a faulty ice chart.

AI4Arctic 海冰挑战数据集由欧洲航天局（ESA）ɸ-lab 启动的 AI4EO 海冰竞赛制作而成。竞赛旨在开发深度学习模型，以自动生成包含海冰浓度、发展阶段及浮冰大小（形态）信息的海冰图。训练数据集包含来自 Sentinel-1 卫星传感器的合成孔径雷达（SAR）数据和相应的被动微波辐射计（MWR）数据。虽然 SAR 数据在开阔水域与海冰之间存在模糊性，但具有较高的空间分辨率；而 MWR 数据在开阔水域与冰面之间具有良好的对比度。然而，AMSR2 MWR 观测的低分辨率引入了一系列新的挑战，例如陆地溢出，可能导致靠近开阔水域的海岸线海冰预测出现误差。挑战数据集中的标签数据为海冰图，由丹麦气象研究所（DMI）的格陵兰冰服务和加拿大冰服务（CIS）为航行安全制作。此外，挑战数据集还包含其他辅助数据，如陆地向距数据和数值天气预报模型数据。场景时间跨度为 2018 年 1 月 8 日至 2021 年 12 月 21 日。数据集存在两种版本，'raw'（原始）和'ready-to-train'（准备训练）版本，以及相应的测试数据集。每个数据集均包含相同的 512 个训练场景和 20 个测试场景（不含标签数据）。"ready-to-train"版本已进一步准备用于模型训练，例如从 40 米像素间距下采样到 80 米，进行标准化缩放，转换冰图（海冰浓度、发展阶段和浮冰大小），去除 NaN 值，掩膜对齐等。本版本为准备训练版本。更多细节描述在随数据集一同发布的通用手册中；“AI4Arctic_challenge-dataset-manual”。'ready-to-train'数据集的入门工具包代码：https://github.com/astokholm/AI4ArcticSeaIceChallenge。有关挑战的快速视频概述可在：https://youtu.be/iuXIeLPyKfg 查看。本项数据集属于集合 https://doi.org/10.11583/DTU.c.6244065。版本 2 增加了 20 个额外场景，并已重新处理以适应更新的均值和标准差（std）。此外，SOD 和 FLOE 变量与版本 1 略有不同，因为主导冰码阈值错误地设置为 70% 和 50%，而非数据集手册中规定的 65%。版本 3 移除了一个含有错误冰图的场景。

搜集汇总

背景与挑战

背景概述

该数据集是为欧洲空间局AI4EO海冰竞赛设计的，包含高分辨率SAR和MWR数据及冰图标签，用于训练海冰自动分类模型。数据集提供经过预处理的'ready-to-train'版本，包含512个训练场景和20个测试场景，覆盖2018至2021年的北极地区。