We deployed our AI platform to detect and classify road-surface damage into three categories — cracks, patches and seams — turning condition assessment into a scalable, data-driven input for maintenance planning and asset management.
The challenge
The client had no systematic, current inventory of road distress. Manual inspection was slow and subjective, put field crews at risk, and offered no automated way to tell damage types apart or judge severity across a whole network.
Our approach
We ran deep-learning models over high-resolution aerial or mobile-mapping imagery, detecting, classifying and geolocating each defect:
- Cracks — fatigue, transverse, edge or random cracking.
- Patches — areas that have been repaired, overlaid or filled.
- Seams — longitudinal construction joints or working cracks along lane boundaries.
Results & benefits
- A comprehensive digital inventory of road damage across the entire network.
- A clear separation of active cracks, repaired patches and construction seams.
- Maintenance zones prioritised by damage density and type.
Sample results
Drag the slider to compare imagery and AI output.
ImageDamage detected
ImageClassified
