UC-215
Asset-Health & Inspection Intelligence
Grades defects from drone, CCTV and LiDAR imagery, forecasts deterioration and plans inspection routes for infrastructure clients.
22Build Duration
5-15xIndicative ROI
The Challenge
Asset inspection and condition assessment is manual, slow and costly, with crews spending days reviewing footage frame by frame. Environmental anomalies stay buried in sensor data.
How It Works
- Ingests drone/CCTV/LiDAR imagery, sensor and SCADA streams and asset registers.
- Deep-learning defect grading, ML deterioration forecasting and route optimisation.
- Publishes graded defect reports, risk-ranked forecasts and client condition dashboards.
What It Removes
- Frame-by-frame inspection review
- Calendar-based inspection scheduling
- Manual environmental anomaly watching
Input Data RequirementsInspection imagery (drone/CCTV), LiDAR/thermal scans, defect taxonomies and condition ratings, sensor/SCADA streams, ArcGIS layers and asset registers
Output FormatAuto-graded defect reports with confidence, deterioration forecasts, optimised inspection schedules, client-facing condition dashboards
“It grades asset condition automatically, predicts what fails next and plans the most efficient inspection rounds.”