Carefully curated and annotated datasets are the foundation of machine learning (ML), with particularly data-hungry deep neural networks forming the core of what is often called artificial intelligence ( AI ). Due to the massive success of deep learning (DL) applied to Earth observation (EO) problems, the focus of the community has been largely on the development of evermore sophisticated deep neural network architectures and training strategies. For that purpose, numerous task-specific datasets have been created that were largely ignored by previously published review articles on AI for EO. With this article, we want to change the perspective and put ML datasets dedicated to EO data and applications into the spotlight. Based on a review of historical developments, currently available resources are described and a perspective for future developments is formed. We hope to contribute to an understanding that the nature of our data is what distinguishes the EO community from many other communities that apply DL techniques to image data, and that a detailed understanding of EO data peculiarities is among the core competencies of our discipline.

中文翻译：

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没有比更多数据更好的数据：地球观测中深度学习的数据集

精心策划和注释的数据集是机器学习 (ML) 的基础，特别需要数据的深度神经网络构成了通常所说的核心人工智能 （ 人工智能 ）。由于深度学习 (DL) 在地球观测 (EO) 问题中的应用取得了巨大成功，社区的重点主要集中在开发更加复杂的深度神经网络架构和训练策略上。为此，创建了许多特定于任务的数据集，这些数据集在很大程度上被之前发表的关于人工智能用于 EO 的评论文章所忽略。通过本文，我们希望改变视角，将专用于 EO 数据和应用程序的 ML 数据集置于聚光灯下。基于对历史发展的回顾，描述了当前可用的资源，并形成了未来发展的前景。我们希望有助于理解我们数据的性质是 EO 社区与许多其他将深度学习技术应用于图像数据的社区的区别所在，并且对 EO 数据特性的详细理解是我们学科的核心能力之一。