On creating benchmark dataset for aerial image interpretation: reviews, guidances and Million-AID

Yang Long, Gui-song Xia, Shengyang Li, Wen Yang, Michael Yang, Xiaoxiang Zhu, Liangpei Zhang, Deren Li

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)
61 Downloads (Pure)


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 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-AID, 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 paper will provide the RS community an overall perspective on constructing large-scale and practical image datasets for further research, especially data-driven ones.
Original languageEnglish
Article number9393553
Pages (from-to)4205-4230
Number of pages26
JournalIEEE Journal of selected topics in applied earth observations and remote sensing
Early online date1 Mar 2021
Publication statusPublished - 2021


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