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Case study

Precision GIS & AR mapping for underground utility infrastructure

iOSLiDARMesh ScanAR (ARKit)SceneKitC++GNSS RTKBluetoothOrthophoto

The problem

Mapping underground utilities without precise positioning creates costly mistakes.

  1. No centimeter-level accuracy
  2. AR markers misaligned with real-world coordinates
  3. No offline capability in zero-signal zones
  4. Surveying and locating handled in disconnected tools

The starting point

Before

  • Standard GPS only, no RTK correction
  • Two workflows with no integration
  • Manual reference point recording
  • No field-grade AR data collection

The challenge

Centimeter accuracy in harsh field conditions + seamless 3D scan across 300 meters + full offline capability.

The solution

Two apps. One unified field mapping system.

01 — Locator: AR asset mapping

  • AR-based utility scanning with real-world coordinate alignment
  • Ticket management + AI data validation
  • Offline-first with opportunistic sync

02 — Surveyor: Precision reference point mapping

  • GNSS + RTK integration, centimeter-level accuracy
  • Reference point surveying with field-grade precision
  • Offline-first with opportunistic sync

Key decisions

1. Rubbersheeting

AR works in screen space. Infrastructure exists in geographic space. We used rubbersheeting to align every AR marker with actual coordinates — without this, centimeter accuracy is impossible.

2. LiDAR + RTK for 300m trench scanning

Combined LiDAR point clouds with RTK-corrected GNSS and long-distance mesh stitching to produce a seamless georeferenced 3D model across the full trench length.

3. Offline-first architecture

Both apps write locally first and sync when coverage returns. Built for underground conditions where connectivity is zero by default.

Results

  • Centimeter-level accuracy for underground asset mapping
  • Seamless 3D model of a 300-meter trench — georeferenced and export-ready
  • Two production apps covering the full field workflow
  • Full offline capability
  • Unified system replacing fragmented manual processes

Ready to discuss your field mapping needs?

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