Using Satellite and Aerial Imagery for Identification of Solar PV: State of the Art and Research Opportunities
Abstract
Solar photovoltaic (PV) is the fastest growing form of energy generation today, and many countries are seeing significant uptake of distributed solar PV on the rooftops of homes and businesses. However, many of these systems are not accurately registered, and central records of distributed solar PV are often not up-to-date. At the same time, high levels of solar PV are introducing challenges for many stakeholders in the energy sector, such as market operators and network operators, who need to forecast total rooftop solar PV generation across entire regions. One possible solution to this problem is to identify existing solar PV generation systems using overhead satellite and aerial imagery. While there have been early promising attempts in this direction, there are nevertheless many important research challenges that remain to be addressed. In this paper we survey the state of the art in this nascent area, describe the challenges that exist, and advocate for novel research questions that are worthy of further exploration. By identifying these areas of interest we aim to generate greater awareness of the potential value of satellite and aerial imagery for identification of solar PV, which will ultimately facilitate large scale uptake of solar PV and other renewable generation technologies.
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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 [165]
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Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
- Authors
- de Hoog, J., Maetschke, S., Ilfrich, P., & Kolluri, R. R.
- Affiliations
- IBM Research Australia
- IBM Research - Australia (AU)
- Venue
- Proceedings of the Eleventh ACM International Conference on Future Energy Systems
- Publisher
- ACM
- Published
- 2020-06-12
- Pages
- 308-313
- OpenAlex
- W3035826718
- Cited by
- 32
- References
- 15
- Citations by year
- 2025: 4 · 2024: 6 · 2023: 5 · 2022: 7 · 2021: 8 · 2020: 2
- FWCI
- 2.10
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Conference on Future Energy Systems (e-Energy) (publishedVersion)
- ACM International Conference on Future Energy Systems (e-Energy)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Solar Radiation and Photovoltaics
- Physical Sciences › Environmental Science › Environmental Engineering › Photovoltaic Systems and Sustainability
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Photovoltaic System Optimization Techniques
- Keywords
- photovoltaic systems, rooftop solar, satellite imagery, aerial imagery, distributed generation, solar generation forecasting, renewable energy mapping
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