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OpenContrails: Benchmarking Contrail Detection on GOES-16 ABI

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arxiv 2304.02122 v2 pith:5L6DHQY6 submitted 2023-04-04 cs.CV

Joe Yue-Hei Ng , Kevin McCloskey , Jian Cui , Vincent R. Meijer , Erica Brand , Aaron Sarna , Nita Goyal , Christopher Van Arsdale
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Scott Geraedts
This is my paper · ORCID
classification cs.CV
keywords contraildetectiondatasetevaluateavoidanceclimatecontrailsgoes-16
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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  1. GVCCS: A Dataset for Contrail Identification and Tracking on Visible Whole Sky Camera Sequences

    cs.CV 2025-07 conditional novelty 7.0 of 10

    GVCCS is the first open dataset of ground-based visible all-sky camera video with instance-level contrail masks, temporal tracking, and flight IDs, plus Mask2Former baselines.

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