  Classification between thousands of classes in high-resolution images is one
of the heavily studied problems in deep learning over the last decade. However,
the challenge of fine-grained multi-class classification of objects in aerial
images, especially in low resource cases, is still challenging and an active
area of research in the literature. Solving this problem can give rise to
various applications in the field of scene understanding and classification and
re-identification of specific objects from aerial images. In this paper, we
provide a description of our dataset - COFGA of multi-class annotated objects
in aerial images. We examine the results of existing state-of-the-art models
and modified deep neural networks. Finally, we explain in detail the first
published competition for solving this task.
