Solar Panel AI
Prudent Partners segmented solar panels in aerial imagery using precise polygon annotations to support a property intelligence platform. The project enabled scalable AI-driven rooftop assessment for energy potential and maintenance planning.
Case Details
Clients: Pixel Art Company
Start Day: 13/01/2024
Tags: Marketing, Business
Project Duration: 9 Month
Client Website: Pixelartteams.com
Let’s Work Together for Development
Call us directly, submit a sample or email us!
Executive Summary
A data annotation project was initiated to improve segmentation of photovoltaic panels in aerial imagery. By implementing a structured polygon labeling workflow, the project significantly enhanced model performance for identifying panel types and reflective surfaces, supporting AI applications in renewable energy and infrastructure analysis.
Introduction
Background
The project involved segmenting RGB imagery of various solar panel layouts and reflective elements from aerial perspectives. The aim was to provide clean, consistent training data for AI models used in infrastructure monitoring and solar asset classification.
Industry
Geospatial AI / Renewable Energy / Visual Infrastructure Analytics
Challenge
Problem Statement
The original datasets were unannotated, with varying angles, lighting conditions, and overlapping panel types. Difficulties included:
- Differentiating visually similar panel types
- Avoiding panel frame/border inclusion in segmentation
- Identifying irregular reflective areas without misclassification
Impact
Imprecise segmentation led to low model accuracy, overfitting, and poor generalization across different terrain and lighting conditions
Solution
Overview
A class-specific, polygon-based annotation strategy was developed to ensure high-resolution object delineation and category accuracy.
Approach
- Four distinct classes were defined: Panel Type 1, Panel Type 2, Panel Type 3, and Reflex Area
- Annotators labeled individual panel instances precisely using polygon tools
- Panel borders and overlapping boundaries were explicitly excluded
- Data was organized under a unified file structure with consistent formatting
- Spot checks and QA sampling were implemented to ensure compliance with annotation standards
- A trained workforce followed in-house SOPs with reference visuals for class distinctions
Results
Outcome
The segmented dataset allowed for significantly improved model performance in identifying and separating different solar panel configurations.
Benefits
- Higher Precision: Clear distinction between overlapping and adjacent panels
- Consistent Data Quality: Reduced labeling noise and increased training reliability
- Scalability: Streamlined processing enabled faster turnaround for larger image batches
Conclusion
Summary
High-resolution, polygon-based annotations are essential for AI models working with complex infrastructure layouts. Structured workflows ensured scalable dataset production and improved segmentation reliability across real-world solar panel images.
Future Plans
- Annotate multispectral and thermal imagery
- Include panel aging/degradation markers to expand model
- capabilities for smart grid monitoring
Call to Action
Organizations in geospatial, energy, and infrastructure AI can benefit from structured polygon annotation processes. Adopting class-specific SOPs and precision workflows ensures accurate, scalable data for high-performing segmentation models