  The development of computer vision algorithms for Unmanned Aerial Vehicles
(UAVs) imagery heavily relies on the availability of annotated high-resolution
aerial data. However, the scarcity of large-scale real datasets with
pixel-level annotations poses a significant challenge to researchers as the
limited number of images in existing datasets hinders the effectiveness of deep
learning models that require a large amount of training data. In this paper, we
propose a multimodal synthetic dataset containing both images and 3D data taken
at multiple flying heights to address these limitations. In addition to
object-level annotations, the provided data also include pixel-level labeling
in 28 classes, enabling exploration of the potential advantages in tasks like
semantic segmentation. In total, our dataset contains 72k labeled samples that
allow for effective training of deep architectures showing promising results in
synthetic-to-real adaptation. The dataset will be made publicly available to
support the development of novel computer vision methods targeting UAV
applications.
