VicRoads – Asset Inventory Survey
The project required the accurate mapping and collection of asset inventory data for a range of assets along 1621.5 km of freeways, highways and main arterial roads under VicRoads responsibility across 13 municipalities.
Asset Inventory Survey
VicRoads maintains an extensive road network across Victoria, broken up into two metropolitan regions and five country regions.
With their overarching focus of safety, the client required an Asset Inventory Survey of their road network in the Metropolitan South East region.
Scope of Services
The project required the accurate mapping and collection of asset inventory data for a range of assets along 1621.5 km of freeways, highways and main arterial roads under VicRoads responsibility across 13 municipalities located in the north east, east and south east of Melbourne.
The project included the following Asset Classes and Sub Asset Classes.
Typical attributes included: Unique Asset ID, Feature, Type, Material, Height, Length/Area, Depth, Diameter, Width, and Comments.
RapidMap used a number of different technologies to maximise safety, accuracy and efficiency in carrying out the survey.
Office Based Digitisation
RapidMap used high-resolution aerial imagery to digitize the location of all visible assets to an accuracy of ± 0.3m. We used MapInfo Professional to carry out the digitising due to its ability to create points, polylines and polygons all in the one GIS layer, thus maximising efficiency in the digitising process.
The asset type was populated during this process, and all other attribution was populated during field inspections or later data extraction.
For freeways, highways and busier roads, RapidMap drove the roads and recorded geo-referenced 360˚ imagery with our mobile mapping system.
A two-person vehicle-based team was used for safety reasons, the roads were driven at, or close to, the relevant speed limit. On dual carriageway roads we drove each carriageway, to collect spatially referenced imagery in both directions.
Where necessary, on wider roads or where traffic blocked clear view to both sides of the carriageway, we also drove the right-hand side of the carriageway to collect imagery in the centre median.
The vehicle mounted mobile mapping system comprises a 360˚ camera, and 8.9-megapixel high-resolution, high sensitivity cameras, coupled with the Applanix POS-LV high accuracy positioning and orientation system comprising 2x GNSS (GPS) linked to an inertial measurement unit (IMU) to ensure accurate location at all times.
The mobile mapping system collected accurately geo-referenced 360˚ imagery which was then processed and used for accurate, safe and efficient collection of asset locations, measurements, and detailed attribution in the office
RapidMap processed the imagery against the GNSS/IMU data so the imagery was fully geo-referenced and optimised for accuracy.
As a desktop exercise, RapidMap reviewed the geo-referenced 360˚ imagery in our software to locate any assets which were not previously digitised, collect any required measurements and populate detailed asset attributes in the office.
On completion of work areas, a series of logical checks were carried out on the data sets. The data was closely scrutinised to ensure that all roads were completed.
We created a series of scripted data integrity checks to be run on the data. These SQL queries were used to identify possible data discrepancies. Any discrepancies uncovered through our office checking procedures were investigated in field. Random checks on the asset data were also carried out in the field.
Field Data Collection
Field data collection was also carried out to complement the mobile mapping data. These were carried out using single person crews with AssetMap software, tablet PCs and DGPS. The field inspections also verified the accuracy of the asset inventory from the office-based digitisation.
Following the desktop data extraction, some of the assets needed field verification, or collection of additional attributes or measurements in the field.
Following completion of all data collection and QC processes, the data was processed against a number of spatial datasets to populate various locational attributes such as LGA, Road Code, Road Name, Road Type and Reference Point Name.
We created coded scripts to process the data against VicRoads’ linear referencing system to populate chainages, and in particular the Distance Past the Reference point, and the Distance Past Start and Distance Past End (the reference points) for the starts and ends of polylines and polygons.
The deliverables included