Advantages
- Eliminates costly and hazardous manual road inspections through full automation
- Scales remotely to evaluate road networks across regions or entire countries
- Improves driver safety with quality metrics, speed guidance, and alternate routes
- Automatically alerts authorities when road damage reaches critical repair thresholds
Summary
Roads are aging, and transportation networks are under constant strain from traffic wear. Yet the methods used to monitor these critical surfaces remain stuck in the past. Infrastructure managers depend on manual crews and costly sensor vehicles to assess road conditions, often working with outdated data that leaves them reacting to failures rather than preventing them. Safety, efficiency, and public resources all pay the price.
This technology takes a fundamentally different approach by using common vehicle oil spots visible in satellite, street level, or vehicle camera imagery as indicators of road distress. Machine learning and image processing work together to classify damage, calculate defect likelihoods, and recommend alternative routes automatically. Unlike conventional inspection methods, this platform scales across entire regions without deploying crews to active roadways. Crowdsourced accelerometer data and user feedback continuously sharpen its predictive models, transforming road monitoring into a proactive, data driven process.
Processed Satellite Image Showing Regions of Oil Accumulation
Desired Partnerships
- License
- Sponsored Research
- Co-Development