Investigating the Value of Synthetic Data to Detect and Classify Aircraft
RarePlanes examines the difference between simulated and synthetic data of computer vision algorithms to detect and classify types of aircraft models in satellite imagery.
RarePlanes is both a machine learning dataset and research study that examines the value of synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. CosmiQ curated a dataset of ~600 WorldView-3 satellite images spanning over 200 locations in 31 countries. It includes ~30,000 manually annotated aircraft and 9 fine grain attributes including: aircraft length, wingspan, wing-shape, wing-position, propulsion, number of engines, number of tail-fins, and aircrafts role. The accompanying synthetic dataset is generated via IQT portfolio company AI.Reverie’s simulation platform and features over 46,000 simulated satellite images with ~300,000 airplane annotations.
The experiments in RarePlanes will address three key areas:
- The performance tradeoffs of computer vision algorithms for detection and classification of aircraft type / model using blends of synthetic and real training data.
- The performance tradeoffs of computer vision algorithms for identification of rare aircraft that are infrequently observed in satellite imagery using blends of synthetic and real training data.
- The value of weakly supervised annotations for detection unique aircraft attributes.