  Airplane detection from satellite imagery is a challenging task due to the
complex backgrounds in the images and differences in data acquisition
conditions caused by the sensor geometry and atmospheric effects. Deep learning
methods provide reliable and accurate solutions for automatic detection of
airplanes; however, huge amount of training data is required to obtain
promising results. In this study, we create a novel airplane detection dataset
called High Resolution Planes (HRPlanes) by using images from Google Earth (GE)
and labeling the bounding box of each plane on the images. HRPlanes include GE
images of several different airports across the world to represent a variety of
landscape, seasonal and satellite geometry conditions obtained from different
satellites. We evaluated our dataset with two widely used object detection
methods namely YOLOv4 and Faster R-CNN. Our preliminary results show that the
proposed dataset can be a valuable data source and benchmark data set for
future applications. Moreover, proposed architectures and results of this study
could be used for transfer learning of different datasets and models for
airplane detection.
