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The Map Layer Your Sensors Cannot Infer

Your vehicles already see the road. What they cannot see is what the road means: which lane connects to which, where the crossing is, which sign governs the junction ahead. MapAtlas produces that layer as data, for every area you operate in.

Built From Imagery, Not Inherited From a Database

Satellite, ground and street-level imagery, reconciled into one picture

Public map data is uneven. A junction mapped in detail in one city is a bare line in the next, and coverage thins exactly where operators are expanding. We combine satellite imagery, ground and street-level imagery and existing map data to produce the layer for a given area, rather than passing on whatever happens to already exist there.

One Schema, Every City You Operate In

The same street is modelled differently depending on where it is. European data tends to record a pavement as an attribute of the road; North American data draws it as separate geometry. Sign conventions diverge the same way. Operators running several regions end up building and maintaining a separate extractor for each one. We normalise it, so the data behaves identically in every market.

Delivered in the Formats Your Stack Already Reads

Bulk extracts per operating region by default, exported to Lanelet2, ASAM OpenDrive, GeoJSON or PBF. Where a runtime dependency suits you better, the same data is reachable through our routing, map matching and isochrone APIs.

What the Layer Contains

Road network and lane connectivity. Sidewalks, footways and crossings. Kerbs and drop-kerbs. Traffic signs, signals, stop and give-way control. Speed limits. Points of interest and address data where your application needs them.

Frequently Asked Questions

Do you supply HD maps?
No. We do not survey centimetre-accurate lane geometry, and most operators we speak to do not need us to: they already run lidar and generate precise local geometry themselves. What lidar cannot infer is meaning. A sensor sees a painted line; it does not know the lane it belongs to connects to the next one, that the crossing ahead is signal-controlled, or what the speed limit is on the road it is joining. That semantic layer is what we produce.
Which formats can we receive the data in?
Lanelet2 and ASAM OpenDrive for autonomous driving stacks, GeoJSON or PBF where a general geospatial format fits better. Bulk extracts per operating region are the default delivery; formats and cadence are set per agreement.
How is the data produced?
By fusing sources rather than relying on any single one. Satellite imagery, ground and street-level imagery and existing map data are combined and reconciled into one picture of an area. This means coverage is not limited to places that happen to be well mapped already, which is the usual constraint when expanding into a new city.
Which regions do you cover?
Data is produced per operating area rather than drawn from a fixed catalogue of supported cities, so coverage follows your expansion rather than constraining it. Our routing and isochrone APIs already answer worldwide, including pedestrian and bicycle profiles.
Where is the data hosted, and how does GDPR apply?
Our infrastructure is hosted in the European Union and we operate as a GDPR-compliant processor. Map and network data of the kind described here contains no personal data. Where your application sends us positions to match or route, that traffic stays within EU infrastructure.

Tell Us Where You Are Driving

Every deployment has its own operating design domain, its own formats and its own expansion plan. The useful first conversation is about where you run today and where you are going next.