  The increasing adoption of solar energy necessitates advanced methodologies
for monitoring and maintenance to ensure optimal performance of solar panel
installations. A critical component in this context is the accurate
segmentation of solar panels from aerial or satellite imagery, which is
essential for identifying operational issues and assessing efficiency. This
paper addresses the significant challenges in panel segmentation, particularly
the scarcity of annotated data and the labour-intensive nature of manual
annotation for supervised learning. We explore and apply Self-Supervised
Learning (SSL) to solve these challenges. We demonstrate that SSL significantly
enhances model generalization under various conditions and reduces dependency
on manually annotated data, paving the way for robust and adaptable solar panel
segmentation solutions.
