TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for
  Terminal Airspace Operations
Jay Patrikar Joao Dantas Brady Moon Milad Hamidi Sourish Ghosh Nikhil Keetha Ian Higgins Atharva Chandak Takashi Yoneyama Sebastian Scherer
We introduce TartanAviation, an open-source multi-modal dataset focused on
terminal-area airspace operations. TartanAviation provides a holistic view of
the airport environment by concurrently collecting image, speech, and ADS-B
trajectory data using setups installed inside airport boundaries. The datasets
were collected at both towered and non-towered airfields across multiple months
to capture diversity in aircraft operations, seasons, aircraft types, and
weather conditions. In total, TartanAviation provides 3.1M images, 3374 hours
of Air Traffic Control speech data, and 661 days of ADS-B trajectory data. The
data was filtered, processed, and validated to create a curated dataset. In
addition to the dataset, we also open-source the code-base used to collect and
pre-process the dataset, further enhancing accessibility and usability. We
believe this dataset has many potential use cases and would be particularly
vital in allowing AI and machine learning technologies to be integrated into
air traffic control systems and advance the adoption of autonomous aircraft in
the airspace.