ResourceType
Application
The transportation side of CSX moves multi-million dollar equipment, from oversized generators to military equipment. To ensure the expensive cargo arrives safely to its destination, CSX first scans the planned route to extract cross-sections of bridges, tunnels, and other overhead obstructions from the LiDAR in order to identify clearance hazards prior to transport.
Results
CSX now owns and operates seven Phoenix LiDAR systems. (4) SCOUT-16 systems mounted on high-rail trucks with Ladybug cameras and (3) SCOUT-32 systems permanently installed on the backs of rolling rail cars. The high-rail trucks can travel on both road and railroad with this flexibility enabling them to go anywhere at any time to support immediate business requests. The rail-based geometry cars are designated to travel and cover the entire 21,000+ mile network at least one time per year as part of their safety protection protocol, with LiDAR continuously collecting data.
Parameters
Site: Rail Corridor
Solution: SCOUT-16 & SCOUT-32
AGL: 4 m
Ground Speed: 25-40 mph
Point Density: 800+ ppsm
Speed: 20-30 mph
Point density: >2000 points/m²
AGL: Ground level
Acquisition time: 1.5 hrs
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In this whitepaper, we explore the innovative application of a hovering drone-mounted LiDAR system paired with a survey-grade satellite and inertial positioning system to measure wave transformation and runup in the surf zone. Unlike traditional methods, the multi-rotor small uncrewed aircraft system (sUAS) offers unobstructed measurements by hovering above the surf zone at a 20-meter elevation, scanning a 150-meter-wide cross-shore transect.
This approach allows rapid and precise data collection in remote locations where terrestrial scanning is challenging. Our study demonstrates that hovering drone-mounted LiDAR provides measurement accuracy almost equivalent to a stationary truck-mounted terrestrial LiDAR. By conducting observations in various surf conditions and validating with traditional land-based surveys and pressure sensors, we achieved a stable back beach topography estimate.
We also calculated statistical wave properties, runup values, and bathymetry inversions using a simple nonlinear correction to wave crest phase speed. This method shows the potential of drone-based LiDAR for accurate nearshore process observations, enabling data collection in previously inaccessible sites and providing valuable validation for coastal models.
Speed: Walking pace for SLAM. 6 m/s UAV flight
Point density: thousands of points/m²
AGL: 80 during UAV flight
Acquisition time: ~2 hours
Speed: 6 m/s
Point Density: 200 points/m² per flight line
AGL: 120 m
Acquisition time: 10 minutes