Solar Panel Detection & Condition Assessment
Converting aerial and satellite imagery into solar asset intelligence by detecting panel locations, coverage patterns and visible condition indicators.
The client needed a faster way to identify and assess solar assets.
Solar asset mapping is often limited by manual inspection, fragmented datasets and slow visual review. This makes it difficult to build reliable inventories, assess rooftop coverage and prioritise follow-up inspections.
AXO GeoAI designed a solar detection workflow to identify visible solar panels from imagery and convert the results into structured geospatial outputs for planning, inventory and reporting use.
What we analysed
From imagery to actionable solar intelligence
Automated panel detection
The workflow identified visible solar panels across imagery and reduced the need for repetitive manual asset review.
Asset inventory generation
Solar locations were structured into geospatial outputs that can support inventories, inspection planning and renewable energy analysis.
Condition assessment support
Visible anomalies, coverage gaps and candidate inspection areas can be flagged depending on imagery resolution and project scope.
Business impact
The workflow supported faster solar asset discovery, improved visibility of distributed panel installations and helped prepare mapping-ready outputs for decision-making.
For energy teams
Faster inventory creation and clearer visibility of solar deployment across rooftops or larger areas.
For inspections
Helps prioritise sites requiring closer review based on visible conditions and spatial patterns.
For planning
Supports renewable energy mapping, asset coverage analysis and opportunity identification.
Final outputs prepared for client use
Need solar panel detection or asset assessment?
Share your imagery, area of interest and required output format. We will review feasibility and prepare a tailored quotation.
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