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Use case: HD maps and digital twins for autonomous vehicles

Self-driving programmes burn enormous time and money on two things: building accurate maps of where they operate, and testing the vehicle against the near-infinite variety of the real world. Earth Observation can shorten both — and because it comes from above, it covers ground a survey car has not driven yet.

The map: localise to the lane

An autonomous vehicle needs to know not just which road it is on, but which lane, and where the markings, signs and stop lines are. Our HD maps deliver that at centimetre accuracy in OpenDRIVE, derived from aerial and street-level imagery and kept fresh with change detection.

Lane-level HD map for autonomous driving
Lane-level geometry and connectivity the planner can route on.

The twin: test before you drive

The same survey becomes a photoreal 3D environment in Unreal Engine. Teams replay real junctions, inject edge cases — a child stepping out, a stalled truck, fog at dusk — and validate perception and planning safely, thousands of times, before a wheel turns.

Photoreal simulation environment for AV testing
The mapped junction, rebuilt as a drivable simulation.

Why one provider for both

  • Map and simulator share the same coordinate frame — what you test is what you drive.
  • Updates propagate from imagery to both layers at once.
  • Open formats keep your toolchain yours.

Mapping a new operating area, or need a scenario library for your sim? Let's talk.