cofga: A Dataset for Fine Grained Classification of Objects from Aerial Imagery
This dataset addresses the challenge of fine-grained classification in overhead images, which is crucial for real-world applications like satellite and airborne imaging, but it is incremental as it builds upon existing datasets.
The authors introduced COFGA, a new open dataset for fine-grained classification of objects in aerial imagery, containing 2,104 high-resolution images with 14,256 annotated objects across 37 distinct labels, which provides higher spatial resolution than most public datasets.
Detection and classification of objects in overhead images are two important and challenging problems in computer vision. Among various research areas in this domain, the task of fine-grained classification of objects in overhead images has become ubiquitous in diverse real-world applications, due to recent advances in high-resolution satellite and airborne imaging systems. The small inter-class variations and the large intra class variations caused by the fine grained nature make it a challenging task, especially in low-resource cases. In this paper, we introduce COFGA a new open dataset for the advancement of fine-grained classification research. The 2,104 images in the dataset are collected from an airborne imaging system at 5 15 cm ground sampling distance, providing higher spatial resolution than most public overhead imagery datasets. The 14,256 annotated objects in the dataset were classified into 2 classes, 15 subclasses, 14 unique features, and 8 perceived colors a total of 37 distinct labels making it suitable to the task of fine-grained classification more than any other publicly available overhead imagery dataset. We compare COFGA to other overhead imagery datasets and then describe some distinguished fine-grain classification approaches that were explored during an open data-science competition we have conducted for this task.