FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery

By Xian Sun, Peijin Wang, Zhiyuan Yan, Feng Xu, Ruiping Wang, Wenhui Diao, Jin Chen, Jihao Li, Yingchao Feng, Tao Xu and 4 more. Isprs Journal of Photogrammetry and Remote Sensing, 2021

563 citations recorded by the source corpus.

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. To establish a baseline for fine-grained object recognition, we propose a novel evaluation method and benchmark fine-grained object detection tasks and a visual classification task using several State-Of-The-Art (SOTA) deep learning-based models on our FAIR1M dataset. Experimental results strongly indicate that the FAIR1M dataset is closer to practical application and it is considerably more challenging than existing datasets.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/6d3c2dc63ff0deec10f60e5a515c93af4f8676f2

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Citations in the corpus

RingMo: A Remote Sensing Foundation Model With Masked Image Modeling (2023, methodology): “1) FAIR1M [4]: It contains more than 1 million instances.”
SkySense: A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery (2023, methodology): “The object detection results on the FAIR1M dataset [65] are visualized in Fig.”
Poly Kernel Inception Network for Remote Sensing Detection (2024, background): “Object detection in remote sensing images (RSIs) has gained substantial attention in recent years [11, 56, 64].”
GeoChat:Grounded Large Vision-Language Model for Remote Sensing (2023, methodology): “Specifically, we integrate three object detection (DOTA [34], DIOR [6], and FAIR1M [27] which together form the SAMRS [30] dataset), one scene classification (NWPU-RESISC-45 [5]), one VQA (LRBEN[20]), and one flood detection [25] VQA dataset (see Table 2).”
EarthGPT: A Universal Multimodal Large Language Model for Multisensor Image Comprehension in Remote Sensing Domain (2024, methodology): “The optical datasets include DIOR [61], DOTA [62], FAIR1M [63], HRRSD [64], NWPUVHR10 [65], RSOD [85], UCAS-AOD [86], and VisDrone [87].”