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[2509.19665] Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy
Summary
This article presents a study on deep learning techniques for detecting clouds and cloud shadows in methane satellite and airborne imaging spectroscopy, highlighting the effectiveness of advanced models over traditional methods.
Why It Matters
Accurate detection of clouds and shadows is crucial for retrieving methane concentrations in remote sensing. This study addresses a significant challenge for the MethaneSAT mission, which aims to improve methane emission quantification through enhanced imaging techniques.
Key Takeaways
- Deep learning models, particularly U-Net and SCAN, significantly enhance cloud detection accuracy compared to traditional methods.
- The MethaneSAT mission requires precise cloud detection to improve methane emission mapping.
- Conventional techniques struggle with spatial coherence and boundary definitions, impacting detection quality.
Computer Science > Computer Vision and Pattern Recognition arXiv:2509.19665 (cs) [Submitted on 24 Sep 2025 (v1), last revised 14 Feb 2026 (this version, v3)] Title:Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy Authors:Manuel Perez-Carrasco, Maya Nasr, Sebastien Roche, Chris Chan Miller, Zhan Zhang, Core Francisco Park, Eleanor Walker, Cecilia Garraffo, Douglas Finkbeiner, Sasha Ayvazov, Jonathan Franklin, Bingkun Luo, Xiong Liu, Ritesh Gautam, Steven Wofsy View a PDF of the paper titled Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy, by Manuel Perez-Carrasco and 14 other authors View PDF HTML (experimental) Abstract:Effective cloud and cloud shadow detection is a critical prerequisite for accurate retrieval of concentrations of atmospheric methane (CH4) or other trace gases in hyperspectral remote sensing. This challenge is especially pertinent for MethaneSAT, a satellite mission launched in March 2024, to fill a significant data gap in terms of resolution, precision and swath between coarse-resolution global mappers and fine-scale point-source imagers of methane, and for its airborne companion mission, MethaneAIR. MethaneSAT delivers hyperspectral data at an intermediate spatial resolution (approx. 100 x 400, m), whereas MethaneAIR provides even finer resolution (approx. 25 m), enabling the development of highly detailed maps of concentrations tha...
