Preprints
https://doi.org/10.48550/arXiv.2307.12032
https://doi.org/10.48550/arXiv.2307.12032
17 Oct 2023
 | 17 Oct 2023
Status: this preprint has been withdrawn by the authors.

Flight Contrail Segmentation via Augmented Transfer Learning with Novel SR Loss Function in Hough Space

Junzi Sun and Esther Roosenbrand

Abstract. Air transport poses significant environmental challenges, particularly regarding the role of flight contrails in climate change due to their potential global warming impact. Traditional computer vision techniques struggle under varying remote sensing image conditions, and conventional machine learning approaches using convolutional neural networks are limited by the scarcity of hand-labeled contrail datasets. To address these issues, we employ few-shot transfer learning to introduce an innovative approach for accurate contrail segmentation with minimal labeled data. Our methodology leverages backbone segmentation models pre-trained on extensive image datasets and fine-tuned using an augmented contrail-specific dataset. We also introduce a novel loss function, termed SR Loss, which enhances contrail line detection by transforming the image space into Hough space. This transformation results in a significant performance improvement over generic image segmentation loss functions. Our approach offers a robust solution to the challenges posed by limited labeled data and significantly advances the state of contrail detection models.

This preprint has been withdrawn.

Junzi Sun and Esther Roosenbrand

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2189', Anonymous Referee #1, 02 Nov 2023
    • AC1: 'Reply on RC1', Junzi Sun, 19 Dec 2023
  • RC2: 'Comment on egusphere-2023-2189', Anonymous Referee #2, 10 Nov 2023
    • AC2: 'Reply on RC2', Junzi Sun, 19 Dec 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2189', Anonymous Referee #1, 02 Nov 2023
    • AC1: 'Reply on RC1', Junzi Sun, 19 Dec 2023
  • RC2: 'Comment on egusphere-2023-2189', Anonymous Referee #2, 10 Nov 2023
    • AC2: 'Reply on RC2', Junzi Sun, 19 Dec 2023
Junzi Sun and Esther Roosenbrand

Model code and software

source code Junzi Sun https://github.com/junzis/contrail-net

Junzi Sun and Esther Roosenbrand

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 186 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
186 0 0 186 0 0
  • HTML: 186
  • PDF: 0
  • XML: 0
  • Total: 186
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 17 Oct 2023)
Cumulative views and downloads (calculated since 17 Oct 2023)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 184 (including HTML, PDF, and XML) Thereof 184 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 15 Jul 2024
Download

This preprint has been withdrawn.

Short summary
Our paper advances contrail segmentation by using augmented transfer learning to overcome data scarcity, a key challenge in environmental monitoring. We introduce a new loss function, significantly boosting contrail detection performance for the proposed neural network model. The model is applicable across diverse imagery, broadens its applicability. Additionally, we release an open-source library that can be beneficial for future research studies.