TartanAviation
TartanAviation is an open-source multimodal dataset focused on terminal-area airspace operations at general-aviation airports, providing a holistic view of the airport environment by concurrently collecting image,…
Source notes
TartanAviation is an open-source multimodal dataset focused on terminal-area airspace operations at general-aviation airports, providing a holistic view of the airport environment by concurrently collecting image, speech, and ADS-B trajectory data using sensor setups installed within airport boundaries. It contains 3.1 million images across 550 sequences (captured with a 4-camera array of Sony IMX264 RGB sensors at 2048x2448 resolution and 24 FPS, stored as MP4/AVI), 3,374 hours of Air Traffic Control (ATC) speech audio (WAV, 44.1 kHz; 477.6 hours above a -20 dB activity threshold), and 661 days of ADS-B aircraft trajectory data (CSV/TXT; ~63 million raw position reports with fields such as ID, timestamp, altitude MSL, speed, heading, lat/long, wind components, range, and bearing). Data were collected at two airfields in the Greater Pittsburgh area: Allegheny County Airport (KAGC, towered) and Pittsburgh-Butler Regional Airport (KBTP, non-towered), spanning multiple months and seasons (vision Dec 2021-Feb 2023 at KAGC; trajectory/speech data Sept 2020-Feb 2023 across both airports) to capture diversity in aircraft operations, aircraft types, and weather. Post-processed (synchronized, filtered, interpolated) versions are also provided. Cameras are static ground-based installations imaging crewed general-aviation aircraft against the sky (no drones/UAVs are involved); no depth, stereo, or fisheye sensing is used. Recording/post-processing/download scripts are released on GitHub, and the dataset is openly hosted on Hugging Face, CMU AirLab servers, and Zenodo.
- Scale
- 3,374 hours
- Formats
- custom
- License
- CC-BY-4.0
- Published
- 2024-03-05
Decision summary
passive_log
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Release history
Source-backed release timing for this canonical dataset record.
- Release evidence
Dataset series published
Release timing is recorded from official dataset metadata.
Selection readiness
6 evidence dimensions for deciding whether this dataset is ready to inspect, compare, or adopt. This is not a model benchmark.
Catalog evidence · not task performance
How scores work69/100
Provisional
3/6 dimensions scored · 41% confidence
Read the evidence behind all 6 dimensions
Access and governance
Access and license are declared by the source.
Schema and signal coverage
No machine-readable schema has been verified yet.
Policy training readiness
Observation/action alignment has not been established.
World-model readiness
Temporal observations plus geometry or semantic context are declared; sample alignment remains to be audited.
Failure and recovery readiness
No verified failure/recovery annotation evidence is available yet.
Download and processing readiness
Scale is declared; transfer and processing estimates are not measured.
Review unresolved evidence and next checks
Signal gaps
- Feedback / correction / failure. Not enough evidence is available to classify this signal.
Next checks
- Read the dataset manifest and feature schema.
- Run a bounded sample audit before assigning Strong readiness.
Engineering checks (OBRS) behind this record
Needs Audit
OBRS metadata and reported test evidence. A breakdown behind Selection readiness, not a separate score or an independent OpenBot certification.
- A passing real-hardware test report is required.
- A passing ingestion/pipeline test report is required.
- A passing privacy and provenance review is required.
Loop signals
6/7 present or partial
No decision-grade evidence captured yet.
unknownView 6 more signal categories
video · TartanAviation is an open-source multimodal dataset focused on terminal-area airspace operations at general-aviation airports, providing a holistic view of the airport environment by concurrently collecting image, speech, and ADS-B trajectory data using sensor setups installed within airport boundaries. It contains 3.1 million images across 550 sequences (captured with a 4-camera array of Sony IMX264 RGB sensors at 2048x2448 resolution and 24 FPS, stored as MP4/AVI), 3,374 hours of Air Traffic Control (ATC) speech audio (WAV, 44.1 kHz; 477.6 hours above a -20 dB activity threshold), and 661 days of ADS-B aircraft trajectory data (CSV/TXT; ~63 million raw position reports with fields such as ID, timestamp, altitude MSL, speed, heading, lat/long, wind components, range, and bearing). Data were collected at two airfields in the Greater Pittsburgh area: Allegheny County Airport (KAGC, towered) and Pittsburgh-Butler Regional Airport (KBTP, non-towered), spanning multiple months and seasons (vision Dec 2021-Feb 2023 at KAGC; trajectory/speech data Sept 2020-Feb 2023 across both airports) to capture diversity in aircraft operations, aircraft types, and weather. Post-processed (synchronized, filtered, interpolated) versions are also provided. Cameras are static ground-based installations imaging crewed general-aviation aircraft against the sky (no drones/UAVs are involved); no depth, stereo, or fisheye sensing is used. Recording/post-processing/download scripts are released on GitHub, and the dataset is openly hosted on Hugging Face, CMU AirLab servers, and Zenodo.
presentTartanAviation is an open-source multimodal dataset focused on terminal-area airspace operations at general-aviation airports, providing a holistic view of the airport environment by concurrently collecting image, speech, and ADS-B trajectory data using sensor setups installed within airport boundaries. It contains 3.1 million images across 550 sequences (captured with a 4-camera array of Sony IMX264 RGB sensors at 2048x2448 resolution and 24 FPS, stored as MP4/AVI), 3,374 hours of Air Traffic Control (ATC) speech audio (WAV, 44.1 kHz; 477.6 hours above a -20 dB activity threshold), and 661 days of ADS-B aircraft trajectory data (CSV/TXT; ~63 million raw position reports with fields such as ID, timestamp, altitude MSL, speed, heading, lat/long, wind components, range, and bearing). Data were collected at two airfields in the Greater Pittsburgh area: Allegheny County Airport (KAGC, towered) and Pittsburgh-Butler Regional Airport (KBTP, non-towered), spanning multiple months and seasons (vision Dec 2021-Feb 2023 at KAGC; trajectory/speech data Sept 2020-Feb 2023 across both airports) to capture diversity in aircraft operations, aircraft types, and weather. Post-processed (synchronized, filtered, interpolated) versions are also provided. Cameras are static ground-based installations imaging crewed general-aviation aircraft against the sky (no drones/UAVs are involved); no depth, stereo, or fisheye sensing is used. Recording/post-processing/download scripts are released on GitHub, and the dataset is openly hosted on Hugging Face, CMU AirLab servers, and Zenodo.
presentTartanAviation is an open-source multimodal dataset focused on terminal-area airspace operations at general-aviation airports, providing a holistic view of the airport environment by concurrently collecting image, speech, and ADS-B trajectory data using sensor setups installed within airport boundaries. It contains 3.1 million images across 550 sequences (captured with a 4-camera array of Sony IMX264 RGB sensors at 2048x2448 resolution and 24 FPS, stored as MP4/AVI), 3,374 hours of Air Traffic Control (ATC) speech audio (WAV, 44.1 kHz; 477.6 hours above a -20 dB activity threshold), and 661 days of ADS-B aircraft trajectory data (CSV/TXT; ~63 million raw position reports with fields such as ID, timestamp, altitude MSL, speed, heading, lat/long, wind components, range, and bearing). Data were collected at two airfields in the Greater Pittsburgh area: Allegheny County Airport (KAGC, towered) and Pittsburgh-Butler Regional Airport (KBTP, non-towered), spanning multiple months and seasons (vision Dec 2021-Feb 2023 at KAGC; trajectory/speech data Sept 2020-Feb 2023 across both airports) to capture diversity in aircraft operations, aircraft types, and weather. Post-processed (synchronized, filtered, interpolated) versions are also provided. Cameras are static ground-based installations imaging crewed general-aviation aircraft against the sky (no drones/UAVs are involved); no depth, stereo, or fisheye sensing is used. Recording/post-processing/download scripts are released on GitHub, and the dataset is openly hosted on Hugging Face, CMU AirLab servers, and Zenodo.
partiallanguage
presentTartanAviation is an open-source multimodal dataset focused on terminal-area airspace operations at general-aviation airports, providing a holistic view of the airport environment by concurrently collecting image, speech, and ADS-B trajectory data using sensor setups installed within airport boundaries. It contains 3.1 million images across 550 sequences (captured with a 4-camera array of Sony IMX264 RGB sensors at 2048x2448 resolution and 24 FPS, stored as MP4/AVI), 3,374 hours of Air Traffic Control (ATC) speech audio (WAV, 44.1 kHz; 477.6 hours above a -20 dB activity threshold), and 661 days of ADS-B aircraft trajectory data (CSV/TXT; ~63 million raw position reports with fields such as ID, timestamp, altitude MSL, speed, heading, lat/long, wind components, range, and bearing). Data were collected at two airfields in the Greater Pittsburgh area: Allegheny County Airport (KAGC, towered) and Pittsburgh-Butler Regional Airport (KBTP, non-towered), spanning multiple months and seasons (vision Dec 2021-Feb 2023 at KAGC; trajectory/speech data Sept 2020-Feb 2023 across both airports) to capture diversity in aircraft operations, aircraft types, and weather. Post-processed (synchronized, filtered, interpolated) versions are also provided. Cameras are static ground-based installations imaging crewed general-aviation aircraft against the sky (no drones/UAVs are involved); no depth, stereo, or fisheye sensing is used. Recording/post-processing/download scripts are released on GitHub, and the dataset is openly hosted on Hugging Face, CMU AirLab servers, and Zenodo.
presentOpen · CC-BY-4.0 · custom
presentEvidence details and provenance
Official signal claims
Schema and annotations
No machine-readable schema facts are captured.
Sample verification
Pending. Metadata does not prove sample coverage, alignment, or file integrity.
curated official source
Integration notes
- Metadata requires review against the official source before publication.