Compact HD Map

Transportation operators, autonomous vehicle developers, and infrastructure planners need highly accurate digital representations of roads and urban environments, but traditional vehicle-based LiDAR surveys are expensive and difficult to scale. Compact HD Map converts high-resolution geospatial imagery into structured vector maps and infrastructure intelligence suitable for autonomous driving, ADAS validation, and smart mobility planning.

GeoMate uses AI-powered feature extraction and semantic interpretation to generate detailed map layers including lanes, intersections, sidewalks, traffic controls, and road attributes. The platform supports city-scale deployment while reducing mapping time and operational costs compared with conventional survey workflows. Maps are delivered in interoperable formats compatible with simulation and GIS ecosystems.

Key capabilities:

• AI Feature Extraction: Automatically identifies lanes, crosswalks, traffic signals, curbs, poles, and road geometry from imagery.
• City-Scale Mapping: Produces structured HD maps across large urban regions in weeks instead of months.
• Infrastructure Intelligence: Generates detailed transportation and mobility datasets for planning and operational analysis.
• Simulation Compatibility: Exports data in formats such as OpenDRIVE, Lanelet2, and GeoJSON.
• High-Precision Localization Support: Enables centimeter-level positioning workflows for ADAS and autonomous systems.

What differentiates GeoMate is its ability to generate scalable HD maps directly from imagery-driven workflows without relying exclusively on costly ground-survey operations.