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On Creating Benchmark Dataset for Aerial Image Interpretation: Reviews, Guidances, and Million-AID
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ( IF 5.5 ) Pub Date : 2021-04-01 , DOI: 10.1109/jstars.2021.3070368
Yang Long , Gui-Song Xia , Shengyang Li , Wen Yang , Michael Ying Yang , Xiao Xiang Zhu , Liangpei Zhang , Deren Li

The past years have witnessed great progress on remote sensing (RS) image interpretation and its wide applications. With RS images becoming more accessible than ever before, there is an increasing demand for the automatic interpretation of these images. In this context, the benchmark datasets serve as an essential prerequisites for developing and testing intelligent interpretation algorithms. After reviewing existing benchmark datasets in the research community of RS image interpretation, this article discusses the problem of how to efficiently prepare a suitable benchmark dataset for RS image interpretation. Specifically, we first analyze the current challenges of developing intelligent algorithms for RS image interpretation with bibliometric investigations. We then present the general guidances on creating benchmark datasets in efficient manners. Following the presented guidances, we also provide an example on building RS image dataset, i.e., Million Aerial Image Dataset (Online. Available: https://captain-whu.github.io/DiRS/0 ), a new large-scale benchmark dataset containing a million instances for RS image scene classification. Several challenges and perspectives in RS image annotation are finally discussed to facilitate the research in benchmark dataset construction. We do hope this article will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones.

中文翻译:

关于创建用于航空影像解释的基准数据集:审查,指南和百万援助

过去几年见证了遥感(RS)图像解释及其广泛应用方面的巨大进步。随着RS图像变得比以往任何时候都更易于访问,对这些图像的自动解释的需求也越来越大。在这种情况下,基准数据集是开发和测试智能解释算法的必要先决条件。在回顾了RS图像解释研究社区中的现有基准数据集之后,本文讨论了如何有效准备适用于RS图像解释的基准数据集的问题。具体来说,我们首先通过文献计量分析来分析开发用于RS图像解释的智能算法的当前挑战。然后,我们介绍以有效方式创建基准数据集的一般指南。https://captain-whu.github.io/DiRS/0 ),这是一个新的大规模基准数据集,其中包含一百万个用于RS图像场景分类的实例。最后讨论了RS图像标注中的一些挑战和观点,以促进基准数据集构建的研究。我们确实希望本文能为RS社区提供有关构建大规模实用图像数据集以进行进一步研究的总体观点,尤其是数据驱动的数据集。
更新日期:2021-05-07
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