Solar Panel Segmentation: Self-Supervised Learning Solutions for Imperfect Datasets

Sankarshanaa Sagaram, Laven Srivastava, Krish Didwania, Aditya Kasliwal, Pallavi Kailas, Ujjwal Verma

Keywords: Solar Panel Segmentation, Self-Supervised Learning, Data Annotation Challenges, SSL Pre-Training, Label Corruption, Machine Learning in Renewable Energy, Cost-Effective Machine Learning

TL;DR: Our paper presents a method for solar panel segmentation using self-supervised learning to overcome challenges of scarce and corrupted data annotations.

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 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.

Submission Number: 16