AI Rooftop Solar Assessment
AI-powered analysis of rooftop solar potential from satellite and aerial imagery
The Problem
“Slow, costly rooftop solar site assessment bottleneck”
Organizations face these key challenges:
Manual rooftop measurements and shading analysis create long lead times and inconsistent quality across assessors and regions
High rate of wasted site visits and redesigns due to inaccurate roof geometry, obstructions, or structural constraints discovered late
Inaccurate production and savings estimates lead to mispriced proposals, customer mistrust, and higher cancellation rates after site survey
Impact When Solved
The Shift
Human Does
- •Review satellite imagery, customer photos, and GIS data to judge roof suitability
- •Collect customer usage details by phone and estimate system size and savings in spreadsheets
- •Schedule and perform site visits to measure roofs and assess shading
- •Revise layouts, quotes, and production estimates after field findings
Automation
- •No meaningful AI support in the legacy assessment workflow
- •Basic mapping layers provide reference imagery for manual review
- •Simple calculators assist with rough production and savings estimates
Human Does
- •Review AI-generated assessments and approve proposals for customer release
- •Handle exceptions when imagery, roof modeling, or property details are incomplete or unreliable
- •Decide when a site visit, design request, or structural review is still required
AI Handles
- •Analyze aerial imagery and LiDAR to identify roof planes, pitch, azimuth, obstructions, and usable area
- •Estimate solar production, bill savings, payback, and carbon impact for viable system sizes
- •Generate standardized rooftop suitability results and design-ready proposal inputs in minutes
- •Detect failed or low-confidence assessments and route them for fallback review or design request
Operating Intelligence
How AI Rooftop Solar Assessment runs once it is live
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not release a customer proposal or quote without human approval of the final system configuration, pricing, and financing assumptions.[S3]
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in AI Rooftop Solar Assessment implementations:
Key Players
Companies actively working on AI Rooftop Solar Assessment solutions:
Real-World Use Cases
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