Automatic Estimation of Solar Rooftops and Power Generation From Publicly Available Satellite Imagery Through Georeferencing and Large-Scale Support
Rooftop photovoltaic (PV) power systems constitute a viable alternative energy technology that can significantly reduce electricity costs. The rapid increase in installations has led to a mismatch between planned power generation and actual electricity demand, necessitating effective monitoring and...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10856154/ |
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| author | John Sullivan Apichon Witayangkurn |
| author_facet | John Sullivan Apichon Witayangkurn |
| author_sort | John Sullivan |
| collection | DOAJ |
| description | Rooftop photovoltaic (PV) power systems constitute a viable alternative energy technology that can significantly reduce electricity costs. The rapid increase in installations has led to a mismatch between planned power generation and actual electricity demand, necessitating effective monitoring and impact assessment. This study proposes a novel approach for detecting solar rooftops using publicly available satellite imagery over large areas. We also introduce a technique for estimating solar panel size and potential energy production, with outputs formatted for GIS applications. Employing a modified U-Net architecture with pre- and post-processing techniques, our experiments achieved an Intersection over Union score of 0.7879 and a Dice score of 0.8808. Image tiling and mosaicking with georeferencing were used to support large-scale imagery. The detection results were post-processed through polygonization and smoothing using the Douglas-Peucker algorithm. Panel size and power generation were then calculated and attached as attributes. Through satellite image analysis, this study aims to accurately identify and evaluate solar rooftops nationwide, providing valuable insights for homeowners, businesses, and government authorities. This facilitates informed decision-making, cost reduction, and contributions to environmental goals. |
| format | Article |
| id | doaj-art-bf8e039ffeb14235b5feb27b8b21234e |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-bf8e039ffeb14235b5feb27b8b21234e2025-02-04T00:00:39ZengIEEEIEEE Access2169-35362025-01-0113207402074910.1109/ACCESS.2025.353581710856154Automatic Estimation of Solar Rooftops and Power Generation From Publicly Available Satellite Imagery Through Georeferencing and Large-Scale SupportJohn Sullivan0Apichon Witayangkurn1https://orcid.org/0000-0003-1454-1820School of Information, Computer, and Communication Technology (I.C.T.), Sirindhorn International Institute of Technology, Thammasat University, Khlong Nueng, Pathum Thani, ThailandSchool of Information, Computer, and Communication Technology (I.C.T.), Sirindhorn International Institute of Technology, Thammasat University, Khlong Nueng, Pathum Thani, ThailandRooftop photovoltaic (PV) power systems constitute a viable alternative energy technology that can significantly reduce electricity costs. The rapid increase in installations has led to a mismatch between planned power generation and actual electricity demand, necessitating effective monitoring and impact assessment. This study proposes a novel approach for detecting solar rooftops using publicly available satellite imagery over large areas. We also introduce a technique for estimating solar panel size and potential energy production, with outputs formatted for GIS applications. Employing a modified U-Net architecture with pre- and post-processing techniques, our experiments achieved an Intersection over Union score of 0.7879 and a Dice score of 0.8808. Image tiling and mosaicking with georeferencing were used to support large-scale imagery. The detection results were post-processed through polygonization and smoothing using the Douglas-Peucker algorithm. Panel size and power generation were then calculated and attached as attributes. Through satellite image analysis, this study aims to accurately identify and evaluate solar rooftops nationwide, providing valuable insights for homeowners, businesses, and government authorities. This facilitates informed decision-making, cost reduction, and contributions to environmental goals.https://ieeexplore.ieee.org/document/10856154/Attribute extractiondeep learningsatellite imagessemantic segmentationsolar panel |
| spellingShingle | John Sullivan Apichon Witayangkurn Automatic Estimation of Solar Rooftops and Power Generation From Publicly Available Satellite Imagery Through Georeferencing and Large-Scale Support IEEE Access Attribute extraction deep learning satellite images semantic segmentation solar panel |
| title | Automatic Estimation of Solar Rooftops and Power Generation From Publicly Available Satellite Imagery Through Georeferencing and Large-Scale Support |
| title_full | Automatic Estimation of Solar Rooftops and Power Generation From Publicly Available Satellite Imagery Through Georeferencing and Large-Scale Support |
| title_fullStr | Automatic Estimation of Solar Rooftops and Power Generation From Publicly Available Satellite Imagery Through Georeferencing and Large-Scale Support |
| title_full_unstemmed | Automatic Estimation of Solar Rooftops and Power Generation From Publicly Available Satellite Imagery Through Georeferencing and Large-Scale Support |
| title_short | Automatic Estimation of Solar Rooftops and Power Generation From Publicly Available Satellite Imagery Through Georeferencing and Large-Scale Support |
| title_sort | automatic estimation of solar rooftops and power generation from publicly available satellite imagery through georeferencing and large scale support |
| topic | Attribute extraction deep learning satellite images semantic segmentation solar panel |
| url | https://ieeexplore.ieee.org/document/10856154/ |
| work_keys_str_mv | AT johnsullivan automaticestimationofsolarrooftopsandpowergenerationfrompubliclyavailablesatelliteimagerythroughgeoreferencingandlargescalesupport AT apichonwitayangkurn automaticestimationofsolarrooftopsandpowergenerationfrompubliclyavailablesatelliteimagerythroughgeoreferencingandlargescalesupport |