Summary of On the Detection Of Aircraft Single Engine Taxi Using Deep Learning Models, by Gabriel Jarry et al.
On the Detection of Aircraft Single Engine Taxi using Deep Learning Models
by Gabriel Jarry, Philippe Very, Ramon Dalmau, Daniel Delahaye, Arthur Houdant
First submitted to arxiv on: 10 Oct 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 paper proposes a novel deep learning approach to detect Single Engine Taxiing (SET) operations using ground trajectory data, specifically focusing on A320 flights. The authors leverage proprietary Quick Access Recorder (QAR) data to label SET or conventional taxiing during taxi-in operations, demonstrating the feasibility of inferring SET from ground movement patterns. This could support more comprehensive environmental impact assessments and improve SET detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SET is a technique that aims to reduce CO2 emissions from ground operations like taxiing in aviation. The paper shows how to use deep learning to detect when an aircraft uses SET, which could help measure its environmental benefits. They used special data from plane operators to teach the model and tested it with similar public data. |
Keywords
» Artificial intelligence » Deep learning
