AI drones for road, traffic, and construction-site monitoring

A transport and infrastructure authority was running three broken workflows in parallel. Road damage was only detected after accidents or citizen reports, with inspection accuracy around 70–80% and small potholes frequently missed. Fixed traffic cameras covered just 5–10% of road corridors, leaving 90% of areas as blind spots, with violation processing taking days to weeks. And manual construction-site surveying took 3–5 days per 50–100 hectares, with measurement errors of ±15–20%.

They needed a single aerial solution that could cover all three – without flooding a central system with raw video.

We built an AI drone platform with Edge AI running on the drone itself to detect road damage, classify maintenance requests by severity with GPS coordinates, patrol for real-time traffic anomalies on live video, and calculate earthwork volumes via 3D Modeling and DSM/DTM generation.

The pipeline runs: Image Data Collection → 3D Modeling & DSM/DTM Generation → Volume Calculation → Result Reporting, with outputs in PDF, CAD (DXF), GeoTIFF, and CSV formats so different teams can plug the results into their existing tooling.

Tech stack: PyTorch, TensorFlow, MLflow, AWS, Azure, Apache Airflow.

Up to 60% reduction in operational costs with faster infrastructure damage detection and dramatically wider surveillance coverage – blind spots closed from 90% down to a fraction of the network.

Reduced risk for field inspection teams, accurate geospatial data for decision-making, and better infrastructure maintenance planning across road, traffic, and construction workflows.