OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI

TL;DR

This paper introduces OpenContrails, a human-labeled dataset and a contrail detection model using GOES-16 ABI data, aiming to facilitate contrail avoidance and reduce aviation's climate impact.

Contribution

It provides the first publicly available contrail dataset and a novel detection model that leverages temporal context for better accuracy.

Findings

The dataset enables effective training and evaluation of contrail detection models.

The proposed model outperforms baseline methods in detection accuracy.

Public availability promotes further research in contrail mitigation.

Abstract

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.