Road Network Intelligence & Mobile Mapping

Yes. Our data outputs align with IPWEA Practice Note 9 and 12.1 guidelines. We provide condition grading on the standard 1-5 scale, ensuring the data can be directly ingested into your 10-year asset management plan and long-term financial reporting. Outputs are formatted for direct import into your existing asset management software.

Most providers capture 2D images that only allow visual defect identification. We deploy vehicle-mounted LiDAR capturing 640,000 points per second simultaneously with 360-degree imagery. You do not just get a photo of a pothole or culvert; you get a measurable digital twin that lets your engineers calculate exact volumes, depths, and clearances from their desks. One pass delivers both the visual record and the engineering-grade spatial data.

Yes. Our mobile mapping systems are mounted on ruggedised 4WD vehicles with trained operators. This allows capture of your entire road network including remote unsealed roads, forestry tracks, fire trails, and regional corridors. No assets are left off your register regardless of terrain or access difficulty.

No. Our vehicle-mounted systems capture data while driving at normal traffic speeds (up to 40 km/h). This eliminates the need for traffic control, road closures, or personnel working on foot near live traffic. Both the cost and the WHS risk of the assessment are significantly reduced compared to traditional methods.

Data is delivered in industry-standard formats including LAS/LAZ (point clouds), GeoTIFF (orthomosaic), GIS-ready shapefiles, geodatabases, and CSV condition registers. These integrate directly with major council platforms including Assetic, Conquest, Reflect, and ESRI ArcGIS. All data is also accessible via our interactive cloud platform where engineers can view 360-degree imagery, measure distances, and export condition reports without specialist software.

For assets that cannot be captured from the road vehicle, our enterprise drone fleet captures bridge undersides, embankments, and elevated structures. For submerged assets such as culvert inverts, dam walls, and stormwater infrastructure, our underwater ROVs provide full visual and sonar inspection without draining or dewatering. All data is unified into a single digital twin of the complete infrastructure corridor.

Road condition assessment using mobile LiDAR and 360-degree imaging typically costs 40-70% less than traditional manual survey methods. Capturing at traffic speed eliminates the need for traffic management plans that can add $2,000-$5,000 per day to traditional approaches. For networks over 250 km, economies of scale reduce the per-kilometre cost significantly. The total program cost includes capture, processing, condition grading to IPWEA Practice Note 9, and delivery via interactive platform.

IPWEA Practice Note 9 is the Australian standard framework for road condition assessment and asset management planning. It defines the 1-5 condition grading scale used by local councils to classify road segments, inform 10-year capital works programs, and support grant applications to state and federal governments. Compliance with PN9 is typically a requirement for Roads to Recovery funding, Financial Assistance Grants, and Fixing Local Roads program submissions.

Yes. The vehicle-mounted LiDAR system captures everything within a 100-metre corridor simultaneously, including footpaths, kerbs, signage, guard rails, drainage pits, vegetation encroachment, and overhead clearances. A single pass at 40 km/h delivers a complete asset inventory of the entire road corridor. Engineers can then extract cross-sections, measure clearances, and identify encroachment issues from their desks without return site visits.

IPWEA recommends condition assessments every 2-4 years for sealed roads and annually for high-traffic corridors. Many councils only achieve 5-10% sample-based coverage due to the cost and disruption of traditional methods. Mobile mapping enables 100% network coverage in a fraction of the time and cost, allowing councils to move from sample-based estimates to complete network intelligence.