OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI
This work addresses the need for contrail detection tools to develop avoidance systems, which could reduce aviation's climate impact, but it is incremental as it builds on existing data and methods.
The paper tackles the problem of automated contrail detection for climate impact reduction by presenting OpenContrails, a human-labeled dataset based on GOES-16 ABI data, and proposes a model with temporal context that achieves improved detection accuracy.
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.