Solutions for Railway
Automated Scan-to-BIM reverse modeling powered by 3D laser point clouds
– delivering high-precision engineering models at minimal cost to boost railway productivity and profitability.
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Integrated Scan2BIM Reverse Modeling
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We use AI and foundation model to provide the infrastructure industry
with high-precision engineering-ready digital models
with high-precision engineering-ready digital models
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The core technology of the German Railway case applies the semantic segmentation function of Wolken Analytics, the core technology of the German Railway case applies the semantic segmentation function of Wolken Analytics, the core technology of the German Railway case applies the semantic segmentation function of Wolken Analytics,
Solutions
The core technology of the German Railway case applies the semantic segmentation function of Wolken Analytics, the core technology of the German Railway case applies the semantic segmentation function of Wolken Analytics, the core technology of the German Railway case applies the semantic segmentation function of Wolken Analytics,
Solutions
The core technology of the German Railway case applies the semantic segmentation function of Wolken Analytics, the core technology of the German Railway case applies the semantic segmentation function of Wolken Analytics, the core technology of the German Railway case applies the semantic segmentation function of Wolken Analytics,
Industry Pain Points
Slow and Costly
Manual Work
Manual modeling dominates the workflow in the absence of automation, leading to slow and costly project delivery.
Unstructured
Data
Rail point cloud datasets are massive and noisy, and they are difficult to clean effectively.
Aging
Infrastructures
Aging infrastructure can hide risks, and manual inspections often miss critical flaws.
Design Phase
Collaboration
Multidisciplinary design workflows suffer from incompatible software tools and file formats.
Key Technologies
Segmentation
Extraction
Reverse Modeling
Railway facilities feature densely distributed components such as rails, ballast beds, overhead contact lines, cable trenches, and equipment foundations, making it difficult to separate and extract their boundaries using traditional methods.
WolkenVision addresses this challenge with a self-developed point cloud semantic segmentation network, integrating geometric features and engineering expertise specific to railway environments to achieve component-level classification of large-scale point clouds.
This module establishes a clear semantic foundation for subsequent attribute recognition and model generation, serving as the critical first step in the Scan2BIM.
By integrating geometric recognition with GIS fusion technology, the system automatically extracts key rail point cloud features—centerline coordinates, boundary lines, and elevation data—while linking spatial geographic information.
This enables intelligent analysis such as structural clearance checks, track deformation trend assessment, and surrounding environment interference detection, enhancing perception, evaluation, and early warning for risks related to railway infrastructure.
The automated reverse modeling process efficiently converts raw point cloud data into Mesh, CAD, and BIM parametric models.
Key steps include component recognition, topological reconstruction, and semantic annotation—ensuring outputs meet the stringent accuracy and compliance standards for railway design, construction, inspection, and maintenance.
Product Process
Successful customers
Swiss Railway Project
WolkenVision implemented 3D laser point cloud solutions for Switzerland's rail network. Using Scan2BIM reverse modeling, we digitally reconstructed tracks and bridges with millimeter precision. Integrated AI analytics enabled real-time structural deformation and defect monitoring.
View Details
90
%Cost Reduction
80
%Time Savings
60
%Labor Reduction
Business Values
Innovating spatial data creation: optimized accuracy, cost-efficiency, and user experience.
Revolutionizing Workflow Velocity
Manual modeling time reduced from weeks to hours,
achieving significant time and labor savings.
achieving significant time and labor savings.
Precise Digital Twins
Creates true-to-life, interactive digital replicas of rail assets,
enabling real-time condition monitoring and predictive maintenance.
enabling real-time condition monitoring and predictive maintenance.
Limitless Applications
Powers smart dispatching, risk forecasting, and O&M optimization,
providing a robust data foundation for intelligent railway ecosystems.
providing a robust data foundation for intelligent railway ecosystems.
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We use AI and foundation model to provide the infrastructure industry
with high-precision engineering-usable digital models
with high-precision engineering-usable digital models
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