DOTAv2.0

Aerial imagery [2], derived from various sources such as drones, satellites, and high-altitude platforms, plays a pivotal role in numerous real-world applications. One of the key tasks is object detection which aims to identify and locate objects of interest within a broader scene. However, the path to robust object detection in aerial images is challenging. Unlike ground-level images that are widely available, aerial data are restricted in dataset volume and generalization across different object classes [2]. An even more pressing concern is the long-tail distribution observed in these datasets, where certain classes of objects are vastly underrepresented. This sparsity of examples and the rare classes poses a significant hurdle for the training of generalized object detectors, as models often struggle to recognize and accurately detect these infrequent objects in real-world scenarios.