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.

翻译：随着太阳能应用的日益普及，需要先进的方法来监测和维护太阳能电池板安装，以确保其最佳性能。在此背景下，从航空或卫星图像中准确分割太阳能电池板是一个关键环节，这对于识别运行问题和评估效率至关重要。本文解决了电池板分割中的重大挑战，特别是标注数据的稀缺性以及监督学习中手动标注的劳动密集型性质。我们探索并应用自监督学习来解决这些挑战。我们证明，自监督学习显著增强了模型在各种条件下的泛化能力，并减少了对人工标注数据的依赖，为构建稳健且适应性强的太阳能电池板分割解决方案铺平了道路。