Use Case: Real-Time Rail Alignment & Terrain Risk Monitoring with Earthbond

Rail infrastructure across Europe and North America faces rising maintenance costs, climate-driven terrain instability, and increasing safety compliance requirements. LiDAR-equipped inspection trains, drones, and stationary sensors now generate terabytes of 3D data every week — but much of it remains geometrically misaligned, fragmented across coordinate systems, and underutilised for predictive risk analysis.

Earthbond solves this by integrating and normalising surface and subsurface spatial data into one unified 3D geodetic environment.
Through ECEF (Earth-Centred, Earth-Fixed) anchoring and CRS normalisation, Earthbond transforms raw LiDAR and IMU feeds into precise, AI-ready datasets that enable operators to detect movement, monitor infrastructure health, and forecast terrain risks in real-time.

Challenge

Railway operators and civil agencies face three core challenges:

Data Fragmentation

Multiple LiDAR and photogrammetry vendors utilise different Coordinate Reference Systems (CRSs) and datums, resulting in positional discrepancies of tens of centimetres or more between datasets.

Subsurface (geotechnical, drainage) data cannot be aligned with surface LiDAR, limiting cross-analysis.

Uncertain Alignment

Track geometry (gauge, cant, elevation) shifts subtly over time due to vibration, soil movement, or frost heave.

Existing monitoring methods rely on batch processing — detecting movement weeks or months after it occurs.

Climate-Driven Terrain Risk

In mountain and alpine regions, increased rainfall and permafrost melt cause mudslides, slope failures, and washouts that can derail trains or damage assets.

Real-time prediction requires the fusion of geotechnical, hydrological, and terrain data into a single, spatially accurate model.

Solution: Earthbond Geodetic Rail Monitoring Framework

1. Unified Data Fusion

Ingests LiDAR, photogrammetry, GNSS/IMU, and subsurface data from trains, UAVs, and field sensors.

Normalises all spatial layers using ECEF coordinates and the correct CRS transformations (ETRS89, WGS84, LV95, or national grids).

Automatically detects and corrects datum and projection errors, ensuring all datasets align to the same Earth-fixed model.

2. Real-Time Correction via API

Existing drilling or steering-like systems on LiDAR inspection trains can send data through the Earthbond REST API.

Earthbond processes the input in real time and returns corrected coordinates, alignment vectors, and uncertainty ellipsoids.

Enables on-the-fly rail geometry correction and anomaly flagging.

3. AI-Driven Movement Analytics

Earthbond’s AI models compare historical and current scans to detect micro-movements (1–5 cm).

Correlates with terrain, weather, and subsurface conditions to predict instability hotspots.

Generates automated alerts and visual 3D dashboards for maintenance teams.

4. Surface + Subsurface Integration

Fuses ground-level data with subsurface geological layers, drainage channels, and underground utilities.

Allows geotechnical teams to model how water saturation or seismic stress could affect track foundations.

Technical Highlights
Capability Description
Coordinate Accuracy <5 cm horizontal & vertical (ECEF-based)
Supported Data Types LiDAR, photogrammetry, InSAR, IMU/GNSS, GPR, soil sensors
API Integration REST/JSON output; compatible with ESRI ArcGIS, Bentley OpenRail, Trimble GEDO
AI Models: Terrain deformation, vegetation encroachment, rail geometry drift
Update Frequency Real-time ingestion or scheduled batch processing
Output Formats 3D digital twin (GLTF/GeoJSON), GIS-ready shapefiles, or API-based dashboards
Impact

For Operators:

Cut inspection lag from months to hours.

Detect rail displacement >1 cm before it becomes critical.

Reduce maintenance costs and downtime through predictive scheduling.

For Regulators:

Deliver traceable, CRS-verified reports for compliance.

Integrate terrain stability with safety risk assessment frameworks.

For Insurers & Asset Managers:

Provide fiduciary-grade geospatial evidence of asset condition.

Enable data-backed risk pricing and ESG reporting for infrastructure funds.

Field Application Example
Swiss Federal Railways (SBB) & Alpine Regions

LiDAR-equipped inspection trains scan 3D corridors through the Alps.

Earthbond ingests the point clouds, applies ECEF geodetic corrections, and compares scans on a monthly basis.

Detected micro-movements of 3–5 cm on a slope near a tunnel entrance, correlating them with InSAR and rainfall data.

Early intervention prevented potential track deformation and service disruption.

Outcomes

Positional accuracy improved by 95% compared to legacy CRS workflows.

Processing time reduced from 4–6 weeks to less than 72 hours.

The predictive accuracy of terrain instability alerts improved to over 90% after AI model calibration.

Next Steps

Earthbond is ready to integrate with existing railway LiDAR and inspection ecosystems through a simple REST API.
Pilot programs are being scoped in Switzerland, Germany, and the UK, where national agencies are modernising digital twins for infrastructure resilience.

Contact:
📩 marcos.vera@toquis.com
| 🌐 www.toquis.com