DeepSolar: A Machine Learning Framework to Efficiently Construct a Solar Deployment Database in the United States
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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.
“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 [868]
Energy-relevant building attributes, such as the presence of photovoltaic panels, can be retrieved from high-resolution satellite images.
“Energy-relevant building attributes, such as the presence of photovoltaic panels, can also be retrieved from these images [868] (see Section 2.1.1).”
From Tackling climate change with machine learning (2023) · cites this paper as [868]
Remote sensing is key to inferring infrastructure data, as satellite data are globally available and largely consistent.
“Remote sensing is key to inferring infrastructure data [79,224,339,496,535,868] as satellite data 19 present a source of information that is globally available and largely consistent worldwide.”
From Tackling climate change with machine learning (2023) · cites this paper as [868]
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References 35 · Cited by 362
- Authors
- Yu, J., Wang, Z., Majumdar, A., & Rajagopal, R.
- Affiliations
- Stanford University (US)
- Corresponding author
- Majumdar, Arun
- Rajagopal, Ram
- Venue
- Joule
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 2
- Issue
- 12
- Pages
- 2605-2617
- ISSN
- 2542-4351 (ISSN-L)
- OpenAlex
- W2904145953
- Cited by
- 362
- References
- 35
- Citations by year
- 2026: 43 · 2025: 69 · 2024: 53 · 2023: 67 · 2022: 46 · 2021: 50 · 2020: 24 · 2019: 8 · 2018: 2
- FWCI
- 11.82
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Joule (publishedVersion)
- Funders
- State Grid of China
- Stanford Interdisciplinary Graduate Fellowship
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Solar Radiation and Photovoltaics
- Physical Sciences › Environmental Science › Environmental Engineering › Photovoltaic Systems and Sustainability
- Physical Sciences › Environmental Science › Global and Planetary Change › Impact of Light on Environment and Health
- Keywords
- solar photovoltaic deployment, satellite imagery, solar panel detection, residential solar adoption, household income, income inequality, Gini coefficient, population density, contiguous United States
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