Smart Road Asset Mapping Solutions

12km of transit corridor transformed into a high-precision digital asset model with 140+ classified infrastructure features. Canadian transit authorities needed a complete digital representation of Dundas Street in Mississauga to support infrastructure planning, maintenance, and future transit expansion. Traditional field surveying methods were slow, high-risk, and unable to efficiently capture the density of road assets required under strict Metrolinx engineering standards. 12km of transit corridor modelled with engineering-grade accuracy. 140+ infrastructure asset classes extracted from mobile LiDAR data. Full compliance achieved with Metrolinx and Inroads design standards.

Mobile LiDAR modelling created a high-precision transit asset database, improving infrastructure planning, maintenance visibility and smart-city readiness across transit corridors.

Industry: Energy & Utilities
Service: Custom Development, Generative AI, Product Engineering

12km: Transit corridor digitally modelled
140+: Road and infrastructure asset classes extracted
100%: Metrolinx specification compliance

Challenge

The project demanded the extraction of highly detailed infrastructure features — including signage, road markings, curbs, utilities, and street furniture — from dense mobile LiDAR point clouds. Every asset needed to align precisely with provincial transit standards and integrate seamlessly into Inroads-compatible engineering environments. The scale and density of the mobile mapping data made manual interpretation both time-intensive and error-prone.

Approach

SBL Infotech deployed a dedicated team of LiDAR specialists using Bentley Descartes and MicroStation workflows to process and model the corridor at engineering precision. Over 140 unique asset classes were extracted, classified, and validated directly against the original point cloud data to ensure millimetre-level spatial accuracy. The workflow produced structured DGN outputs fully aligned with Metrolinx topographic specifications and infrastructure symbology standards.

Outcome

The client gained a scalable digital asset management framework capable of supporting transit planning, maintenance operations, and future smart-city infrastructure initiatives. What was once dependent on manual surveying became a structured, query-ready 3D engineering environment — reducing operational risk, improving asset visibility, and accelerating infrastructure decision-making across the transit network.

IX Case studies

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