Abstract
Contrails, artificial clouds formed at high altitudes when aircraft fly through air in certain conditions, are one of the largest causes of global warming in the aviation industry. Specifically, persistent contrails - those which last for longer than 20 minutes - can be especially harmful to the environment due to their reflective nature, which traps heat around the atmosphere. This paper focuses on utilizing multiple transfer learning ComputerVision models, as well as the first ever use of Visual Transformers in this field, to detect contrails in the environment using a ground based dataset. Our results showed success, rivaling and achieving state of the art models in terms of accuracy. Key Words: Contrails, ComputerVision, Visual Transformers, Global Warming
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