Case study · Geomatics & linear engineering

Pavement condition mobile mapping

We fused laser crack-measurement data, IMU-located imagery and IRI profiles into a per-100 m pavement condition database with automated defect proposals, halving rater review load while keeping recall above 95% across 2,800 km of arterials.

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Context

The situation.

Regional transport contractor responsible for condition grading of 2,800 km of arterial roads.

Scope

Fusion of laser crack-measurement data, IMU-located imagery and IRI profiles into a per-100 m segment condition database with automated defect proposals (cracking, ravelling, potholes) and a review workstation for certified raters.

Outcome

Network-wide condition refresh within 48 h of survey completion, defensible for maintenance budget hearings.

Stack
PythonPostGISNext.js
Challenges

What made it hard.

Defect proposals overwhelmed raters with false positives in patched sections: we trained the proposal filter on the contractor’s own accepted grades, halving review load while keeping recall above 95%.

FAQ

Common questions.

What was the scope?

Fusion of laser crack-measurement data, IMU-located imagery and IRI profiles into a per-100 m segment condition database with automated defect proposals (cracking, ravelling, potholes) and a review workstation for certified raters.

Who was the client?

Client names are withheld under confidentiality agreements. References are available on request.

What was the outcome?

Network-wide condition refresh within 48 h of survey completion, defensible for maintenance budget hearings.

Related capability

Data & MLOps

Ingestion, warehousing, pipelines, model serving, drift monitoring.

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Trust

This is one of roughly fifty engagements we ship per year. The full record, 2011 to today, lives on the delivery history page.

Have a similar problem?

Fixed scope, senior engineers, staging from week one.

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