  Semantic segmentation has been one of the leading research interests in
computer vision recently. It serves as a perception foundation for many fields,
such as robotics and autonomous driving. The fast development of semantic
segmentation attributes enormously to the large scale datasets, especially for
the deep learning related methods. There already exist several semantic
segmentation datasets for comparison among semantic segmentation methods in
complex urban scenes, such as the Cityscapes and CamVid datasets, where the
side views of the objects are captured with a camera mounted on the driving
car. There also exist semantic labeling datasets for the airborne images and
the satellite images, where the top views of the objects are captured. However,
only a few datasets capture urban scenes from an oblique Unmanned Aerial
Vehicle (UAV) perspective, where both of the top view and the side view of the
objects can be observed, providing more information for object recognition. In
this paper, we introduce our UAVid dataset, a new high-resolution UAV semantic
segmentation dataset as a complement, which brings new challenges, including
large scale variation, moving object recognition and temporal consistency
preservation. Our UAV dataset consists of 30 video sequences capturing 4K
high-resolution images in slanted views. In total, 300 images have been densely
labeled with 8 classes for the semantic labeling task. We have provided several
deep learning baseline methods with pre-training, among which the proposed
Multi-Scale-Dilation net performs the best via multi-scale feature extraction.
Our UAVid website and the labeling tool have been published https://uavid.nl/.
