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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.03372 |
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| _version_ | 1866908281301106688 |
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| author | Patrikar, Jay Dantas, Joao Moon, Brady Hamidi, Milad Ghosh, Sourish Keetha, Nikhil Higgins, Ian Chandak, Atharva Yoneyama, Takashi Scherer, Sebastian |
| author_facet | Patrikar, Jay Dantas, Joao Moon, Brady Hamidi, Milad Ghosh, Sourish Keetha, Nikhil Higgins, Ian Chandak, Atharva Yoneyama, Takashi Scherer, Sebastian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_03372 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operations Patrikar, Jay Dantas, Joao Moon, Brady Hamidi, Milad Ghosh, Sourish Keetha, Nikhil Higgins, Ian Chandak, Atharva Yoneyama, Takashi Scherer, Sebastian Machine Learning 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. |
| title | TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operations |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2403.03372 |