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

TL;DR

This paper introduces GVCCS, a comprehensive dataset of contrail videos with flight data, and a deep learning framework for contrail segmentation and tracking, aiming to improve climate impact modeling.

Contribution

The paper presents a new open dataset with detailed contrail annotations and a unified deep learning model for contrail analysis, enhancing observational capabilities.

Findings

Dataset contains 122 sequences and 24,228 frames with flight identifiers.

The deep learning model performs segmentation and tracking in a single architecture.

Supports improved contrail monitoring and climate impact assessments.

Abstract

Aviation's climate impact includes not only CO2 emissions but also significant non-CO2 effects, especially from contrails. These ice clouds can alter Earth's radiative balance, potentially rivaling the warming effect of aviation CO2. Physics-based models provide useful estimates of contrail formation and climate impact, but their accuracy depends heavily on the quality of atmospheric input data and on assumptions used to represent complex processes like ice particle formation and humidity-driven persistence. Observational data from remote sensors, such as satellites and ground cameras, could be used to validate and calibrate these models. However, existing datasets don't explore all aspect of contrail dynamics and formation: they typically lack temporal tracking, and do not attribute contrails to their source flights. To address these limitations, we present the Ground Visible Camera Contrail Sequences (GVCCS), a new open data set of contrails recorded with a ground-based all-sky camera in the visible range. Each contrail is individually labeled and tracked over time, allowing a detailed analysis of its lifecycle. The dataset contains 122 video sequences (24,228 frames) and includes flight identifiers for contrails that form above the camera. As reference, we also propose a unified deep learning framework for contrail analysis using a panoptic segmentation model that performs semantic segmentation (contrail pixel identification), instance segmentation (individual contrail separation), and temporal tracking in a single architecture. By providing high-quality, temporally resolved annotations and a benchmark for model evaluation, our work supports improved contrail monitoring and will facilitate better calibration of physical models. This sets the groundwork for more accurate climate impact understanding and assessments.