From Maintenance To Management, Drone Mapping Hits The Road

HIGHWAY MAINTENANCE CAN BE TIME-CONSUMING and expensive, not to mention an aggravation for delayed or rerouted drivers. That may be changing soon, thanks to drones and photogrammetry software in the geospatial industry that allow dynamic, strategic planning and operations. Commonly referred to as “drone mapping,” the combination of these technologies allows organizations to perform frequent inspections and create up-to-date, visual asset databases that are rich in actionable insights.

 

From an operational point of view, keeping an up-to-date database and performing periodic surveys of roads, bridges, and other civil engineering structures have traditionally been considered costly and time-consuming. Using drones as the data-capture tool and photogrammetry software to transform this data into digital spatial models addresses this operational blocker.

Vassilis Polychronos, CTO of GeoSense, is a drone-mapping convert, and he shared his thoughts on using drone mapping for asset management in Motorway A5 in northern Greece. He says, “The goal of the project was to produce documentation for asset management of 6.8 miles of new highway and an as-built survey on a radius of 262 feet around it. Our client required an accuracy of less than 3 inches.

“The highway is located on a plain surrounded by mountains. The significant variations in terrain elevation posed a challenge. With the requested accuracy level in mind, we had to plan and adapt our mission to these topographic conditions.

“We used a senseFly eBee Plus RTK drone with a S.O.D.A. [SensorOptimised for Drone Application] camera. To plan the mission, we used eMotion, the planning software from senseFly, which is capable of designing corridor-mapping missions following SRTM [shuttle radar topography mapping] DTMs [digital terrain models].

“In less than two hours, we captured 1,300 images, mapping 6.8 miles of highway and covering an area of over 14 square miles. The receiver in the eBee Plus provided solid and highly accurate geotags to the images. We processed the dataset using Pix4Dmapper software on a desktop device. The resulting product had a mean ground sample distance of 1.24 inches. We assessed the accuracy of the project using four checkpoints, and the results were well below the accuracy [maximum] required by our client. We delivered a 3D point cloud in LAS format, a 3D textured mesh in FBX format, an orthomosaic, and a DSM.