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

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

TL;DR

The paper tackles the challenge of segmenting solar panels in satellite imagery when annotated data are scarce and often corrupted. It leverages self-supervised learning, specifically SimCLR pretraining on unlabeled imagery, to learn robust representations that improve segmentation performance when fine-tuned on labeled data. The approach is evaluated on PV03 and cross‑dataset settings with architectures like U‑Net and PSPNet, showing that SSL can produce masks closer to real-world structures than corrupted ground truths and generalize across datasets. This work suggests a cost‑effective, scalable path for robust solar panel monitoring and maintenance using limited 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.

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

TL;DR

The paper tackles the challenge of segmenting solar panels in satellite imagery when annotated data are scarce and often corrupted. It leverages self-supervised learning, specifically SimCLR pretraining on unlabeled imagery, to learn robust representations that improve segmentation performance when fine-tuned on labeled data. The approach is evaluated on PV03 and cross‑dataset settings with architectures like U‑Net and PSPNet, showing that SSL can produce masks closer to real-world structures than corrupted ground truths and generalize across datasets. This work suggests a cost‑effective, scalable path for robust solar panel monitoring and maintenance using limited 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.
Paper Structure (9 sections, 2 equations, 1 figure, 2 tables)

This paper contains 9 sections, 2 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Results
  4. Conclusion
  5. Self-Supervised Learning and SimCLR
  6. Loss function
  7. Pretraining as a method to generalize across datasets
  8. SolarDK Dataset
  9. Generalizability

Figures (1)

  • Figure 1: Instances demonstrating corrupted ground truths in the datasets, with contrast-enhanced RGB images for easier visualization, and how SSL adapts in the learning process.