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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 a cost-effective way for large-scale rooftop solar potential analysis when compared to other data sources. Existing studies mainly utilize aerial imagery and convolutional neural networks to learn the roof segmentation mask or the rooftop geometry map, which are the preliminary input for rooftop solar potential estimation. However, these methods fail to achieve precise solar potential analysis results. To address this issue, we propose a framework, which is termed as SolarNet for rooftop solar potential estimation. A novel multi-task learning network is devised in SolarNet to learn our proposed novel representation for rooftop geometry that incorporates 6 roof segments and orientations. Specifically, this network first learns a roof segmentation map, and then together with the extracted multiscale and contextual features to learn a roof geometry map. Finally, the solar potential can be estimated from the learned roof geometry map. The effectiveness of SolarNet is validated on two datasets: DeepRoof and RID datasets. Experimental results demonstrate that SolarNet can improve not only rooftop geometry prediction accuracy but also solar potential estimation precision, which significantly outperforms other competitors.