Contrails (condensation trails) are line-shaped ice clouds caused by aircraft
and are likely the largest contributor of aviation-induced climate change.
Contrail avoidance is potentially an inexpensive way to significantly reduce
the climate impact of aviation. An automated contrail detection system is an
essential tool to develop and evaluate contrail avoidance systems. In this
paper, we present a human-labeled dataset named OpenContrails to train and
evaluate contrail detection models based on GOES-16 Advanced Baseline Imager
(ABI) data. We propose and evaluate a contrail detection model that
incorporates temporal context for improved detection accuracy. The human
labeled dataset and the contrail detection outputs are publicly available on
Google Cloud Storage at gs://goes_contrails_dataset