Machine vision for natural gas methane emissions detection using an infrared camera
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Previous and ongoing work has used sensor and/or satellite data to proactively suggest pipeline maintenance or detect existing leaks.
“Previous and ongoing work has used sensor and/or satellite data to proactively suggest pipeline maintenance [213,898] or detect existing leaks [81,749,823,826]”
From Tackling climate change with machine learning (2023) · cites this paper as [826]
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A review of green artificial intelligence: Towards a more sustainable future
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Tackling climate change with machine learning
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- Authors
- Wang, J., Tchapmi, L. P., Ravikumar, A. P., McGuire, M., Bell, C. S., Zimmerle, D., Savarese, S., & Brandt, A. R.
- Affiliations
- Stanford University (US)
- Colorado State University (US)
- Corresponding author
- Wang, Jingfan
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 257
- Pages
- 113998
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2988600867
- Cited by
- 205
- References
- 57
- Citations by year
- 2026: 43 · 2025: 48 · 2024: 41 · 2023: 24 · 2022: 22 · 2021: 17 · 2020: 10
- FWCI
- 6.31
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Applied Energy (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- arXiv (Cornell University)
- Funders
- Stanford Natural Gas Initiative
- Topics
- Physical Sciences › Environmental Science › Global and Planetary Change › Atmospheric and Environmental Gas Dynamics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Aerospace Engineering › Infrared Target Detection Methodologies
- Keywords
- methane leak detection, natural gas emissions, optical gas imaging, infrared camera, machine vision, convolutional neural networks, background subtraction, methane plume detection, GASNet
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