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

• URL: http://arxiv.org/abs/2105.12786v1
• Date: Wed, 26 May 2021 18:39:47 GMT
• Title: cofga: A Dataset for Fine Grained Classification of Objects from Aerial Imagery
• Authors: Eran Dahan, Tzvi Diskin, Amit Amram, Amit Moryossef, Omer Koren
• Abstract summary: 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. The 14,256 annotated objects in the dataset were classified into 2 classes, 15 subclasses, 14 unique features, and 8 perceived colors.
• Score: 2.169919643934826
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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