A comprehensive review of optical remote-sensing image object detection datasets
With the introduction of artificial-intelligence technologies such as deep learning into the field of optical remote-sensing detection, various algorithms have emerged. The use of these algorithms has gradually formed a new paradigm of data-driven optical remote-sensing image object detection. Consequently, high-quality remote-sensing data has become a prerequisite and a necessary resource for researching these paradigm algorithms. highlighting the increasing importance of remote-sensing data. To date, numerous optical remote-sensing image object detection datasets have been published by major research institutions domestically and internationally. These datasets have laid the foundation for the development of deep learning-based remote-sensing image detection tasks. However, no comprehensive summarization and analysis of the published optical remote-sensing image detection datasets have been conducted by scholars. Therefore, this paper aimed to provide a comprehensive review of the published datasets and an overview of algorithm applications. We also aimed to provide a reference for subsequent research in related fields.This paper presents an overview and synthesis of the optical remote-sensing image object detection datasets published between 2008 and 2023. The synthesis is based on an extensive and comprehensive survey of literature in the field. By reviewing and analyzing these datasets, we enable a comprehensive understanding of the progress and trends in optical remote-sensing image object detection dataset research.This paper categorizes the optical remote-sensing image object detection datasets published from 2008 to 2023 based on the annotation method. A comprehensive description of 11 representative datasets is provided, and all dataset information are summarized in tabular form. The analysis considers the information in the datasets themselves and also the spatial and spectral resolution of the images in the datasets. Other basic information including the number of categories, number of images, number of instances, and image-width information are also considered. This analysis effectively demonstrates the trend toward high quality, large scale, and multi-category development of object-detection datasets for optical remote-sensing images. Additionally, we provide an overview of the development and application of algorithms related to published datasets from different perspectives (e.g., horizontal bounding box object detection and rotated bounding box object detection), as well as a subdivision of detection directions (e.g., small object detection and fine-grained detection). Our findings confirm the influential role of remote-sensing data in driving algorithmic advances.In summary, we offer a comprehensive review of optical remote-sensing image object detection datasets from various perspectives. To our best knowledge, this comprehensive review is the first one on such datasets in the field. The work serves as a valuable reference for subsequent research on deep learning-based optical remote-sensing image object detection, providing insights into data availability and research directions. This study is expected to contribute to the advancement of this field by offering a solid foundation for further investigation and innovation.
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