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Automatic detection of solar photovoltaic arrays in high resolution aerial imagery
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“Since the locations and sizes of rooftop solar panels are often unknown to electricity system operators, previous work has used computer vision techniques on satellite imagery to generate size and location data for rooftop solar panels [165,514,868].”
From Tackling climate change with machine learning (2023) · cites this paper as [514]
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“[90] Jordan M Malof, Kyle Bradbury, Leslie M Collins, and Richard G Newell.”
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Metadata
- Authors
- Malof, J. M., Bradbury, K., Collins, L. M., & Newell, R. G.
- Affiliations
- Duke University (US)
- Corresponding author
- Malof, Jordan M.
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 183
- Pages
- 229-240
- ISSN
- 0306-2619 (ISSN-L)
- OpenAlex
- W2494424066
- Cited by
- 196
- References
- 76
- Citations by year
- 2026: 17 · 2025: 29 · 2024: 23 · 2023: 32 · 2022: 28 · 2021: 23 · 2020: 19 · 2019: 8 · 2018: 8 · 2017: 7 · 2016: 2
- FWCI
- 6.86
- 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
- Wells Fargo
- Alfred P. Sloan Foundation
- Topics
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Solar Radiation and Photovoltaics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Photovoltaic Systems and Sustainability
- Keywords
- solar photovoltaic array, very high resolution satellite imagery, aerial imagery, automated detection algorithm, solar energy mapping, solar resource assessment
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