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Detection and classification of objects in overhead images are two importantand challenging problems in computer vision. Among various research areas inthis domain, the task of fine-grained classification of objects in overheadimages has become ubiquitous in diverse real-world applications, due to recentadvances in high-resolution satellite and airborne imaging systems. The smallinter-class variations and the large intra class variations caused by the finegrained nature make it a challenging task, especially in low-resource cases. Inthis paper, we introduce COFGA a new open dataset for the advancement offine-grained classification research. The 2,104 images in the dataset arecollected 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, 15subclasses, 14 unique features, and 8 perceived colors a total of 37 distinctlabels making it suitable to the task of fine-grained classification more thanany other publicly available overhead imagery dataset. We compare COFGA toother overhead imagery datasets and then describe some distinguished fine-grainclassification approaches that were explored during an open data-sciencecompetition we have conducted for this task.

cofga: A Dataset for Fine Grained Classification of Objects from Aerial Imagery