  RarePlanes is a unique open-source machine learning dataset that incorporates
both real and synthetically generated satellite imagery. The RarePlanes dataset
specifically focuses on the value of synthetic data to aid computer vision
algorithms in their ability to automatically detect aircraft and their
attributes in satellite imagery. Although other synthetic/real combination
datasets exist, RarePlanes is the largest openly-available very-high resolution
dataset built to test the value of synthetic data from an overhead perspective.
Previous research has shown that synthetic data can reduce the amount of real
training data needed and potentially improve performance for many tasks in the
computer vision domain. The real portion of the dataset consists of 253 Maxar
WorldView-3 satellite scenes spanning 112 locations and 2,142 km^2 with 14,700
hand-annotated aircraft. The accompanying synthetic dataset is generated via
AI.Reverie's simulation platform and features 50,000 synthetic satellite images
simulating a total area of 9331.2 km^2 with ~630,000 aircraft annotations. Both
the real and synthetically generated aircraft feature 10 fine grain attributes
including: aircraft length, wingspan, wing-shape, wing-position, wingspan
class, propulsion, number of engines, number of vertical-stabilizers, presence
of canards, and aircraft role. Finally, we conduct extensive experiments to
evaluate the real and synthetic datasets and compare performances. By doing so,
we show the value of synthetic data for the task of detecting and classifying
aircraft from an overhead perspective.
