Aircraft contrail emission is widely believed to be a contributing factor to global climate change. We have used machine learning techniques on images containing contrails in hopes of being able to identify those which contain contrails and those that do not. The developed algorithm processes data on contrail characteristics as captured by long-term image records. Images collected by the United States Department of Energy’s Atmospheric Radiation Management user facility(ARM) were used to train a deep convolutional neural network for the purpose of this contrail classification. The neural network model was trained with 1600 images taken by the Total Sky Imager(TSI) from March 2017 and achieved an accuracy of 97.5% on the training set of images and an accuracy of 98.5% on the validation set.
"Atmospheric Contrail Detection with a Deep Learning Algorithm,"
Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal: Vol. 7
, Article 5.
Available at: https://digitalcommons.morris.umn.edu/horizons/vol7/iss1/5