  Addressing airport traffic jams is one of the most crucial and challenging
tasks in the remote sensing field, especially for the busiest airports. Several
solutions have been employed to address this problem depending on the airplane
detection process. The most effective solutions are through the use of
satellite images with deep learning techniques. Such solutions, however, are
significantly costly and require satellites and modern complicated technology
which may not be available in most countries worldwide. This paper provides a
universal, low cost and fast solution for airplane detection in airports. This
paper recommends the use of drones instead of satellites to feed the system
with drone images using a proposed deep learning model. Drone images are
employed as the dataset to train and evaluate a mask region convolution neural
network (RCNN) model. The Mask RCNN model applies faster RCNN as its base
configuration with critical modifications on its head neural network
constructions. The model detects whether or not an airplane is present and
includes mask estimations to approximate surface area and length, which will
help future works identify the airplane type. This solution can be easily
implemented globally as it is a low-cost and fast solution for airplane
detection at airports. The evaluation process reveals promising results
according to Microsoft Common Objects in Context (COCO) metrics.
