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AI USE CASE

Roof Damage Photo Classifier

Automatically classify roof damage from photos and generate insurance-ready reports for roofing contractors.

Minimum data maturity
basic
Technical prerequisite
spreadsheet savvy
Industries
Cross-industry
AI type
computer vision

What it is

A computer vision model analyses drone or ladder photos to identify damage types, hail, wind, wear, or structural, and generates a formatted customer and insurer report in minutes. Setup requires a modest labelled photo dataset of past jobs or a pre-trained roofing model fine-tuned on local conditions.

Data you need

A collection of labelled roof photos (minimum ~200–500 images across damage categories) from past jobs, drone footage, or publicly available roofing datasets.

Required systems

  • none

Why it works

  • Start with a pre-trained roofing or construction damage model and fine-tune on your own job photos rather than building from scratch.
  • Define a simple photo capture protocol for crews (angles, lighting, required shots per area) to ensure consistent input quality.
  • Generate reports in a format insurers and customers already expect, PDF with annotated photos, damage type labels, and severity ratings.
  • Keep a roofer in the loop to validate classifications before reports are sent, especially for large or complex claims.

How this goes wrong

  • Too few labelled training images leads to poor classification accuracy, especially for subtle damage types like early-stage wear.
  • Inconsistent photo quality (bad lighting, angle, resolution) from field crews degrades model performance in real conditions.
  • Contractors skip adoption if the report output format doesn't integrate naturally into their existing quoting or claims workflow.
  • Over-reliance on automated classification without roofer review leads to errors in customer-facing reports and potential liability.

When NOT to do this

Don't build a custom computer vision pipeline from scratch if your team has fewer than 10 employees and no in-house developer, the maintenance burden will outweigh the time savings within months.

Example vendors

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