SolarNet: A convolutional neural network-based framework for rooftop solar potential estimation from aerial imagery

• Published in: International journal of applied earth observation and geoinformation Vol. 116; p. 103098
• Main Authors: Li, Qingyu, Krapf, Sebastian, Shi, Yilei, Zhu, Xiao Xiang
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2023; Elsevier
• Subjects: Convolutional neural network; cost effectiveness; data collection; energy; geometry; people; prediction; Remote sensing; Renewable energy; Roof segments and orientations; solar energy; Solar potential; spatial data; sustainable development; Roof segments and orientations; Solar potential; Renewable energy; Convolutional neural network; Remote sensing

Solar power is a clean and renewable energy source. Promoting solar technology can not only offer all people affordable, reliable, and modern energy, but also mitigate energy-related emissions and pollutants. This significantly contributes to sustainable development goals. Aerial imagery can provide...