MOBILE ROADWAY LIDAR COLLECTION ACROSS THE UNITED STATES

Field collection, system troubleshooting, and data-quality review for large-scale transportation mapping.

Mandli Communications | Mobile Mapping | 2020–2021

THE ASSIGNMENT

Mandli deployed vehicle-mounted LiDAR and high-resolution roadway-imaging systems to document transportation corridors across a wide range of landscapes and operating conditions. The work required reliable collection at roadway speeds while maintaining coverage, system performance, and field safety.

MY ROLE

I operated and supported mobile mapping systems during extended field assignments across the United States. My responsibilities included preparing collection vehicles, monitoring LiDAR and imagery acquisition, following planned roadway routes, managing field data transfers, documenting field conditions, and checking that each run produced usable data.

FIELD OPERATIONS

The work moved through urban corridors, mountain highways, open prairie, and remote rural roads. Every environment introduced different challenges: traffic, changing light, weather, limited pull-offs, long travel days, and equipment behavior under continuous operation.

SYSTEM TROUBLESHOOTING & QUALITY CONTROL

I diagnosed collection interruptions, reviewed imagery and sensor outputs, confirmed coverage, maintained collection equipment, and helped determine when routes required recollection. This connected field operations directly to downstream data quality and showed me how small hardware, positioning, or documentation issues can affect large geospatial datasets.

FOUNDATION FOR LIDAR WORK

The experience gave me a practical foundation in mobile LiDAR, roadway imagery, large-scale collection logistics, field data management, and quality assurance. It also shaped how I approach today’s UAV, point-cloud, and remote-sensing workflows: collect carefully, document decisions, and verify the data before it moves downstream.

CAPABILITIES DEMONSTRATED

Mobile LiDAR collection · Roadway imagery · Field logistics · Sensor monitoring · Collection troubleshooting · Coverage review · Data quality control · Large-dataset organization