Power pole networks span thousands of kilometres. Manual inspection requires ground-level crews, captures inconsistent data, and gives asset managers limited visibility into the true condition of the network.
Large networks with thousands of poles make manual survey cycles too slow to keep pace with deterioration.
Ground-level inspection misses defects at height. Climbing requires specialist crews, harnesses and site shutdowns.
Manual inspection quality varies between crews, shifts and weather conditions, making trending unreliable.
Manual field data collection requires hours of post-inspection report compilation before maintenance can be prioritised.
Drones capture multi-angle imagery of every pole, systematically recording its condition across defect types such as cracking, corrosion, insulator condition, crossarm and bird nesting, with the detection library expanding as new classes are added. Flight waypoints are logged at the same time, so the exact mission can be re-flown for automated repeat inspections.
Every image is analysed two ways. An asset survey identifies the pole and all of its subcomponents, and AI defect detection classifies each defect by type, severity and the component it sits on. The model set isn't fixed, new defect and asset classes can be trained and added as needs evolve.
Every detection is confirmed, flagged or dismissed by a reviewer, then automated per-pole condition and asset survey reports are generated without manual compilation, each linked to pole ID and GPS coordinates.
Severity ratings are stored in our asset management database, and poles are ranked by defect severity and criticality so maintenance teams receive a prioritised work order list.
Drone imagery, AI findings, severity ratings and asset records come together in the NXTGEN platform. Every detection is confirmed, flagged or dismissed before it reaches a report.
Our platform learns from your defect catalogue, so classifications match your standards and the detection library keeps expanding as new classes are added.
Surface and structural cracks across pole shaft
Rust, oxidation and metal degradation at base and fittings
Chipped, cracked or contaminated insulators
Sag, cracks and mounting integrity
Hazardous nesting on active equipment
Missing, damaged or corroded earthing components
Structural splitting of timber pole shafts, with early detection before failure risk escalates.
Pole lean beyond acceptable tolerances, identified from drone imagery without ground measurement.
Cross arm deflection, rotation or misalignment affecting conductor clearance and load distribution.
Vegetation within statutory clearance zones, mapped and prioritised for vegetation management crews.
Debris, equipment or objects lodged on conductors, cross arms or pole structure.
Fire and heat damage to pole shaft, hardware and fittings, classified by severity.
Conductor sag beyond clearance thresholds, identified across spans from aerial survey data.
Every defect is scored by severity and risk, giving your maintenance team a prioritised, evidence-backed work order list.
Our platform learns your asset build components from your database, so when imagery is captured it checks what is actually on the structure against what is recorded in your asset management system.
Identifies and records foundation configuration for each pole in the network.
Classifies pole material, timber, concrete, steel or composite, automatically from imagery.
Records pole structure configuration, single, H-frame, guyed or otherwise, per network survey.
Identifies insulator type and records specification data alongside condition assessment, and counts insulator disks per string with their configuration.
Detects and records vibration dampers fitted to the span.
Identifies non-network attachments such as lights and other third-party objects on the structure.
Reads and records pole name plates and asset ID plates for automated asset matching.
Detects and records network signage and asset markers on the structure.
Cover more poles per day than ground crews. Survey entire network segments without lane closures or traffic management.
Detection is configured and validated against your own defect catalogue, so classifications match your standards and apply the same way to every pole, every time.
No manual report writing. Each pole gets a complete inspection record automatically linked to your asset register.
Maintenance teams receive a ranked work order list, highest risk poles first, with evidence attached.
Full transparency of risk across your entire network, every defect, every pole, every severity rating in one view. No gaps, no blind spots.
Every survey captures full asset attributes alongside defects, foundation type, pole material, insulator specification and structure type, building a living asset register automatically.
Identify degradation trends before they become failures. Move from reactive to condition-based maintenance scheduling across your network.
Findings, severity ratings and asset records push into your asset management system via API, so your system of record stays current automatically.
Logged flight waypoints let you re-fly the exact mission over time, so condition is compared like-for-like and degradation is trended.
Every finding is backed by source imagery and an AI confidence score, giving a defensible, audit-ready record for compliance and spend justification.
We scope your taxonomy and data structures, pole types, defect classifications and asset register requirements configured to match your network.
The platform is configured to your specific assets and environment conditions. Your team is onboarded on the system.
Initial survey conducted. AI models calibrated and validated against your pole types and environment. Outputs reviewed before go-live.
Automated survey, reporting and condition database delivered to your operations team. Ongoing survey cycles run on your schedule, and AI models are tuned continuously as new inspection data comes in, so detection keeps improving over time.
Tell us about your pole network and inspection requirements.
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