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A groundbreaking tool named SolarNet+ has been developed to estimate rooftop photovoltaic (PV) potential in cities. This innovative framework utilizes deep learning to analyze aerial images, taking into account roof superstructures like windows and chimneys. By doing so, it significantly improves the accuracy of solar potential assessments compared to existing methods.
The tool was tested in cities like Munich and Brussels, demonstrating superior performance in predicting roof orientations and superstructures. Researchers aim to enhance its transferability across different urban environments by collecting diverse training samples. Future improvements will focus on addressing domain shifts to ensure accurate predictions in various city landscapes.
• SolarNet+ uses deep learning for accurate rooftop solar potential estimation.
• The tool considers roof superstructures to improve prediction accuracy.
This technique is utilized in SolarNet+ to analyze aerial images for estimating rooftop solar potential.
SolarNet+ employs CNNs to learn roof orientation and superstructure maps from aerial imagery.
This method is applied in SolarNet+ to enhance the learning of roof orientations and superstructures.
The university's researchers developed SolarNet+ to improve urban solar potential assessments.
The center contributed to the development of the SolarNet+ framework for estimating rooftop solar potential.
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