  Contrails, short for condensation trails, are line-shaped ice clouds produced
by aircraft engine exhaust when they fly through cold and humid air. They
generate a greenhouse effect by absorbing or directing back to Earth
approximately 33% of emitted outgoing longwave radiation. They account for over
half of the climate change resulting from aviation activities. Avoiding
contrails and adjusting flight routes could be an inexpensive and effective way
to reduce their impact. An accurate, automated, and reliable detection
algorithm is required to develop and evaluate contrail avoidance strategies.
Advancement in contrail detection has been severely limited due to several
factors, primarily due to a lack of quality-labeled data. Recently, proposed a
large human-labeled Landsat-8 contrails dataset. Each contrail is carefully
labeled with various inputs in various scenes of Landsat-8 satellite imagery.
In this work, we benchmark several popular segmentation models with
combinations of different loss functions and encoder backbones. This work is
the first to apply state-of-the-art segmentation techniques to detect contrails
in low-orbit satellite imagery. Our work can also be used as an open benchmark
for contrail segmentation and is publicly available.
