Summary of Tartanaviation: Image, Speech, and Ads-b Trajectory Datasets For Terminal Airspace Operations, by Jay Patrikar et al.
TartanAviation: Image, Speech, and ADS-B Trajectory Datasets for Terminal Airspace Operations
by Jay Patrikar, Joao Dantas, Brady Moon, Milad Hamidi, Sourish Ghosh, Nikhil Keetha, Ian Higgins, Atharva Chandak, Takashi Yoneyama, Sebastian Scherer
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed TartanAviation dataset aims to revolutionize airport operations by providing a comprehensive, multi-modal data repository. The dataset combines images, speech, and ADS-B trajectory data collected within airport boundaries, offering a holistic view of terminal-area airspace operations. With over 3 million images, 3374 hours of Air Traffic Control speech data, and 661 days of ADS-B trajectory data, TartanAviation is an extensive resource for researchers and developers in the field of aviation AI. The dataset’s diversity in aircraft operations, seasons, aircraft types, and weather conditions makes it particularly valuable for training and validating machine learning models. By opening-sourcing the code-base used to collect and pre-process the dataset, the authors have further enhanced its accessibility and usability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TartanAviation is a big project that aims to help airplanes fly safely by collecting lots of data from airports. They got images, speech recordings, and information about planes’ paths all at once! This helps us understand how airports work better. The data was collected in many different places, times, and weather conditions, which makes it very useful for people who want to teach computers to predict what will happen in airport situations. The project also shares the code they used to collect and prepare the data, making it easier for others to use. |
Keywords
* Artificial intelligence * Machine learning * Multi modal
