SolarPanel Segmentation :Self-Supervised Learning for Imperfect Datasets

arXiv:2402.12843v1 Announce Type: new
Abstract: 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 …

abstract adoption advanced aerial arxiv context cs.ai cs.cv datasets energy maintenance monitoring panel panels performance satellite segmentation self-supervised learning solar solar panels supervised learning type