A point cloud captures reality in high detail – but a survey plan does not require millions of individual points; it requires clearly defined geometric elements. A road edge should be available as a line. A guardrail should be captured as a coherent object. An embankment needs an upper and lower boundary. Road markings must be available as lines or polygons.

This is exactly the transformation our AI performs: it analyzes the three-dimensional geometry, classifies relevant structures, and generates coherent, survey-ready geometries from them. The manual effort for digitization, tracing, and structuring is significantly reduced.

Not every object has the same geometric properties in a point cloud. A guardrail differs fundamentally from a road marking or an embankment. Our solution therefore combines Machine Learning, 3D geometry analysis, and rule-based post-processing – depending on the object class, different features such as height differences, local slopes, normal vectors, and spatial continuity are evaluated.

If intensity values from the LiDAR system or suitable image data are available, road markings can also be detected automatically. Geometry and reflectance or image information are combined to achieve reliable detection results. The result is CAD-ready lines or polygons with a defined object class, which can be directly integrated into existing planning and GIS workflows.

Embankments can require considerable manual processing effort. Our solution analyzes local height changes, slopes, normal vectors, and the three-dimensional terrain shape. Continuous polylines are generated from the detected areas – precisely and without time-consuming manual tracing.

Vertical and linear structures have characteristic geometric properties by which they can be reliably classified from the point cloud. The required object classes are defined before the project begins and can be extended on a project-specific basis – making the solution adaptable to individual infrastructure requirements.

The input data is analyzed and structured for automated processing. Large road corridors are automatically divided into manageable sections, enabling even extensive survey campaigns to be processed efficiently. Thorough data validation is essential to ensure reliable results in the subsequent processing steps.

From the detected point group, the geometry that is actually relevant for surveying is extracted. This step transforms the raw point set into defined, metrically usable geometric primitives – varying by object class:

Individual point groups or line segments must be connected into consistent polylines. The system takes into account direction, distance, curvature, and geometric plausibility. Redundant vertices are then reduced and the geometries are optimized for CAD and GIS.

The generated objects are classified, attributed, and exported to the desired output format. This transforms an unstructured point cloud into a fully structured digital road inventory – ready for direct use in CAD, GIS, and asset management systems.

For long road sections, it is not only important which object was detected, but also where along the route it is located. Our processing can therefore automatically divide detected lines into defined route segments – with freely selectable segment lengths of 10, 12.5, 25 meters, or project-specific values. Each segment receives its own ID and attributes for GIS, inventory, and asset management.

Real road networks are complex. Intersections, driveways, side roads, traffic islands, or closely adjacent objects can lead to ambiguities. Our processing therefore does not look at individual points in isolation – the AI always takes spatial context into account.

Our goal is not to replace the surveyor. The AI primarily takes over repetitive and time-consuming digitization work. Professional oversight remains an integral part of the process. This means the AI handles a large portion of the manual work, while surveying responsibility is preserved.

The generated geometries are provided in the formats actually used in existing surveying, CAD, and GIS workflows. Layer structure, attribute schema, and data organization can be adapted to customer-specific standards.

2D or 3D polylines with a defined layer structure for use in AutoCAD, BricsCAD, and other CAD systems. Layer names and data structure can be fully adapted to the customer's existing CAD standards.

GIS-ready output for direct use in existing geographic information systems and infrastructure registry platforms. The geometries are delivered with a complete attribute schema – ready for integration into operational GIS workflows.

Our solution does not replace existing surveying or CAD systems. It specifically automates the time-consuming processing step between point cloud and CAD or GIS. The workflow remains compatible with existing tools and data standards.

The economic benefit of automation grows with the volume of data. A few hundred meters of road can still be processed manually – but at 20, 50, or 150 kilometers, manual digitization becomes the bottleneck. Our processing is therefore designed from the ground up for large data volumes.

The solution covers the full spectrum from one-time as-built surveys to continuous infrastructure management. Every application benefits from the same automated processing core – adapted to the specific requirements.

Our solution is provided as a Cloud Service by default. This allows even large point clouds to be processed without the customer needing to set up additional GPU or AI infrastructure. New survey campaigns can be automatically uploaded, processed, and delivered as finished results.

For projects with particularly high requirements for data protection, information security, or data sovereignty, the entire processing can also be operated fully On-Premise within the customer's own infrastructure.

Point clouds can contain highly detailed information about roads, structures, and critical infrastructure. Controlled handling of this data is therefore an integral part of our solution – not an afterthought.

Customer data is used exclusively for the agreed processing. It is not used for further development or training of AI models without the customer's explicit consent. This ensures the customer retains full control over how their data is used.

Depending on the project, all essential processing steps can be documented. This keeps it traceable which data, processed in which way, led to which result – an important requirement for quality assurance, auditability, and long-term data quality.

Depending on the customer environment, we support different security and integration requirements. The technical architecture is adapted to the project's security specifications – not the other way around.

LiDAR and mobile mapping today generate high-quality 3D data at an ever-increasing scale. The bottleneck is increasingly no longer in data capture, but in processing. Our AI automates exactly this step.