Blue Marble Shows How AI and LiDAR Deliver ROI for Rail Operators

A New Moment for BVLOS Operations

Drone operators in the U.S. now have more access to Beyond Visual Line of Sight (BVLOS) waivers than ever before. These waivers allow a single operator to fly longer routes without placing observers along the entire path. The FAA’s recent publication of the BVLOS Notice of Proposed Rulemaking (NPRM) also marks the first step toward routine BVLOS operations.

For linear assets like railways, BVLOS is a game changer. It lets teams capture long corridors in fewer flights, collect consistent data with less downtime, and reduce operational cost. It also improves safety by keeping workers out of hazardous areas. As these approvals expand, more operators can survey large rail networks with the efficiency that industry stakeholders have long sought.

Better Sensors and Smarter Software

This regulatory progress comes at the same time drone platforms are improving. New aircraft support higher-density LiDAR, longer endurance, and more advanced navigation. These upgrades allow operators to gather detailed rail data in a single mission and at a scale that was not possible only a few years ago.

But raw data alone does not deliver value. The real impact comes from converting that data into information that engineers and planners can use. That is where Blue Marble Geo is delivering in the new drone landscape.

Extracting Rail Lines from LiDAR: A Practical Workflow

Blue Marble Geo has long focused on helping professionals manage and extract value from complex geospatial datasets. Their workflow for extracting railroad tracks from LiDAR demonstrates how targeted tools, automated selection methods, and quality checks can convert dense point clouds into accurate engineering products.  See Blue Marble’s full workflow here.

Step 1: Prepare and Classify the Point Cloud

The first step is to limit the dataset to the area surrounding the rails. Users can draw an area feature around the track and apply Crop to Selected Areas to remove unneeded surroundings. If cropping isn’t preferred, most tools allow users to set bounds and restrict processing to a defined region.

Next, the point cloud is classified to remove vegetation, buildings, and noise. Ground classification should keep the rails included; otherwise, they will not appear in the elevation model used later. This step produces a cleaner dataset in which the rails begin to stand out.

Step 2: Generate the DEM for Breakline Extraction

With the points cleaned, the Create Elevation Grid tool produces the DEM that the breakline algorithm relies on. Users often filter by ground points to create a smoother, more accurate surface. Because the method depends on slope and elevation contrast, the DEM must be detailed enough for the rails to appear as a clear rise above the ballast. The Path Profile tool helps confirm that the elevation change is captured correctly.

Step 3: Extract Rail Features Using Breaklines

Rail extraction occurs in the Generate Breaklines tool. For this analysis, the Find Breaklines at Any Surface Breakmethod is most effective because it detects the sharp curvature changes created by rail edges.

Key settings include:

• Curvature Grid Type: Profile – generates lines that follow the rails
• Edge Detect Threshold (~500) – isolates steep slopes typical of steel rail edges
• Edge Connect Threshold (<300) – links small gaps to produce continuous lines

These parameters guide the software in tracing each rail as a clean, parallel vector.

Step 4: Validate, Adjust if Needed, and Export

If breaklines appear incomplete, users can produce a dedicated Curvature Grid to verify that resolution and parameters are appropriate. Comparing grids at different resolutions can show whether the DEM captures the rails’ signature well enough for extraction.

Once validated, the rail lines can be exported to CAD, GIS, or digital twin platforms for design, maintenance planning, and change detection.

This streamlined workflow reduces manual digitization and produces consistent results across long rail corridors—an important advantage as BVLOS operations and high-density LiDAR collection become more common.

Why This Matters in the BVLOS Era

The growth of BVLOS approvals and the momentum behind the NPRM mean more operators can now collect LiDAR at corridor scale. Blue Marble Geo’s automation ensures that this increase in data collection does not create new bottlenecks. Instead, it helps teams convert large point clouds into usable information at speed.

The combination of BVLOS flight, longer drone endurance, better sensors, and automated feature extraction creates new ROI for rail operators and mapping firms. Teams can move from raw LiDAR to actionable outputs faster. They can build digital twins, model rail geometry, track changes over time, and support predictive maintenance.

In the past, long-distance drone mapping was limited by rules and hardware. Today, it is becoming common. Blue Marble Geo provides the processing power needed to match that reality.

A Glimpse at the Future of Industrial Mapping

The rail-extraction example is one workflow, but it points to a larger shift. Drone operations continue to expand, and more missions involve long corridors. AI and automation are reducing the time between data capture and decision-ready outputs. Regulations are also evolving to match industry needs.

Blue Marble Geo sits at the center of these trends. The company’s tools help users extract clean features, automate repeatable work, and build accurate digital models from drone and aerial LiDAR. As BVLOS operations grow, workflows like rail extraction will only become more important.

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