KIRIOLL

Welcome to a webpage of a project dedicated to developing an innovative solution for creation highly automated digital data models of large-scale linear structures. By integrating advanced 3D scanning technology with AI, this project aims to produce accurate representations of highways and their components, enabling the automated generation of detailed engineering drawings and 3D BIM and GIS models.

Let's find out how we do it:

Our goals

High-Precision Data Collection

We collect accurate, high-quality data along extensive stretches of highways or similar linear structures. This data serves as a reliable foundation for model-based outputs.

Automating Data Processing

Large point cloud datasets can be time-consuming to process manually. Our aim is to automate as much of this workflow as possible, reducing the labor required and making data handling more efficient.

Scalable for Large Infrastructure

Highways can span thousands of kilometers. We are developing processes capable of capturing and analyzing data for virtually limitless lengths of these structures.

Delivering Comprehensive Results

We strive to provide high-precision outputs in multiple forms, such as 2D plans, 3D models, asset inventories, and GIS data.

Vermessungsbüro Rink GbR, Lang&Lang GmbH and ioLabs AG share a passion for smart, effective solutions and have joined forces to improve existing workflows to make the production process faster and more accurate. We want to effectively process large amounts of data — thousands of kilometers of highway sections to be 3D scanned and documented — in the shortest possible time and to develop an innovative solution for digital data models of large linear structures.

It all starts with 3D scanning

At the heart of this project, we are using advanced technologies that have great potential for automation. 3D scanners Riegl Vux 1Ha are capable of collecting large amounts of accurate, large-scale data. Combined with AI that can sort, process, and produce the data, it is possible to generate vector representations of highway objects in various data types.

Point clouds

Our advanced Mobile Laser Scanning (MLS) system ensures complete, high-precision highway mapping by covering every lane and side-line at ±30° angles, eliminating data gaps. Georeferenced every 250 meters with precise control points, the MLS data is seamlessly integrated with drone flights, capturing large and hard-to-reach areas.

By combining these technologies, we generate highly accurate .las point clouds, refining the data by removing temporary obstacles and filling shadow gaps. The result? A seamless, true-to-life digital representation with minimal deviation from real-world dimensions.

The System

Our powerful tool transforms raw point cloud data into highly accurate highway models with a streamlined, three-module process. Module 1 filters irrelevant data and extracts highway objects, generating an XML-based exchange file with precise mathematical definitions. Module 2 converts this into CAD models with 90-95% accuracy, highlighting any imperfections detected in previous step. Module 3 then refines and corrects these areas, achieving a fully accurate native model. A built-in feedback loop continuously enhances the process, ensuring ever-improving precision for future data analysis.

Step 1 - Extraction

Module 1 analyzes raw point cloud data to create a precise mathematical model of key road features, including edges, guardrails, and lane markings. Using the RANSAC algorithm, it detects the highway ground plane and filters out irrelevant data. High-reflectivity road markings are identified through peak analysis, while the DBSCAN algorithm clusters lane markings. Machine learning then refines object recognition, fitting splines to define lane shapes and precisely positioning guardrails in 3D.

The extracted data is formatted into an XML exchange file with geometric and semantic details. Uncertain detections are flagged with visual markers, guiding Module 3 in refining problematic areas. A built-in feedback loop continuously enhances accuracy, ensuring high-precision object recognition for seamless highway modeling.

Step 2 - Modeling

Module 2 transforms the exchange file into high-precision vector geometry and 3D models, seamlessly integrating with industry-standard software like Autodesk Revit, Civil 3D, Rhinoceros 3D, and ESRI tools. The data is sorted by geometry type and reconstructed into accurate CAD elements, starting with linear features enriched with metadata. These elements serve as the foundation for complex 3D surfaces, representing roadways, crash barriers, and lane markings.

The final output is a fully structured CAD file, exported in formats like DWG or IFC, ensuring compatibility with various CAD and GIS platforms. Enriched with detailed metadata, these models provide a complete, data-driven representation of highway infrastructure, ready for further analysis and design integration.

Step 3 - Revision

Module 3 enhances the accuracy of highway models by refining elements that automated processes may have missed. Starting with a CAD model containing visually marked gaps, users can manually adjust, add, or correct missing details with precision. This hands-on refinement not only ensures a complete and error-free model but also feeds back into the system, improving the accuracy of previous modules, particularly Module 1.

A key feature of Module 3 is its dynamic feedback loop. Every manual adjustment helps the system learn, optimizing future model-building workflows and reducing the need for manual intervention over time. Additionally, we explore AI-driven opportunities to accelerate model refinement, such as improving geometry extraction in Module 1. While AI implementation is not the primary goal, these insights contribute to greater efficiency, paving the way for smarter, more automated infrastructure modeling.

Key features

One-Button Solution

Our approach minimizes manual intervention by automatically processing raw input data into structured outputs. A manual quality check remains possible, feeding back into the system to continuously enhance overall process quality.

Compatibility

We support a wide range of industry-standard formats, ensuring seamless integration with existing workflows. From descriptive 2D plans and asset lists to complex 3D models and GIS datasets, our outputs fit diverse project needs.

Efficiency

By leveraging automated object recognition and machine learning, we significantly reduce processing time for long linear structures. This automation ensures faster data generation compared to manual workflows while maintaining high accuracy.

Process ability

Beyond delivering final outputs, we focus on generating data that remains adaptable for further processing. Our results are compatible with standard CAD and GIS software, enabling flexible use across different applications.

Use of outputs

We are able to seamlessly integrate output into Common Data Environments (CDEs), such as ACC, with both geometry and rich meta-data. Once imported, the models are automatically converted into CDE objects, enabling effortless management and further processing tailored to specific use cases. And all that in compliance with ISO 19650.

This streamlined workflow empowers this tool to cover the entire cycle — from data capture to operation — ensuring efficiency and precision at every stage.

Result

Once all the steps described are completed, there are a variety of output options. We produce accurate 3D models with all defined elements in place and correctly georeferenced. Our models contain all the necessary data to be used throughout the production process. 3D models are also converted into high quality GIS data, allowing the creation of large scale models that can be supplemented with other GIS layers such as terrain or various types of maps. The generated geometry is also converted to 2D plans for more conventional use cases.

2D plans
- Standardized 2D drawings in PDF and DWG formats
- Additional formats available upon request
Asset Collection
- Comprehensive list of objects on and alongside the highway
- Provides a robust foundation for CAFM
3D BIM Model
- Developed using Autodesk Civil 3D, Revit, or Rhinoceros 3D
- Additional formats available upon request
GIS Model
- Advanced ArcGIS Pro and ArcGIS Online models
- Additional formats available upon request