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FAIR1M: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery

Sun, Xian; Wang, Peijin; Yan, Zhiyuan; Xu, Feng; Wang, Ruiping; Diao, Wenhui; Chen, Jin; Li, Jihao; Feng, Yingchao; Xu, Tao; Weinmann, Martin 1; Hinz, Stefan 1; Wang, Cheng ; Fu, Kun
1 Institut für Photogrammetrie und Fernerkundung (IPF), Karlsruher Institut für Technologie (KIT)

Abstract:

With the rapid development of deep learning, many deep learning-based approaches have made great achievements in object detection task. It is generally known that deep learning is a data-driven method. Data directly impact the performance of object detectors to some extent. Although existing datasets have included common objects in remote sensing images, they still have some limitations in terms of scale, categories, and images. Therefore, there is a strong requirement for establishing a large-scale benchmark on object detection in high-resolution remote sensing images. In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15,000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery which is named as FAIR1M. All objects in the FAIR1M dataset are annotated with respect to 5 categories and 37 sub-categories by oriented bounding boxes. Compared with existing detection datasets dedicated to object detection, the FAIR1M dataset has 4 particular characteristics: (1) it is much larger than other existing object detection datasets both in terms of the quantity of instances and the quantity of images, (2) it provides more rich fine-grained category information for objects in remote sensing images, (3) it contains geographic information such as latitude, longitude and resolution, (4) it provides better image quality owing to a careful data cleaning procedure. ... mehr


Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Forschungsbericht/Preprint
Publikationsdatum 09.03.2021
Sprache Englisch
Identifikator KITopen-ID: 1000146309
Umfang 19 S.
Nachgewiesen in Dimensions
arXiv
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