Vaisala

NCTech offers a service that collects high-resolution 360° street-level imagery and LiDAR data to document infrastructure assets in the field. It uses geospatial capture techniques to map the exact location, orientation, and status of items like signs, poles, utility boxes, sidewalks and more. The data collection process replaces many site visits by enabling desktop review of assets, and feeds into updating inventories and condition assessments with minimal manual input. The captured imagery and 3D data can be regularly refreshed so agencies can monitor change over time and integrate the outputs into GIS or asset-management systems.

RoadVision AI uses imagery from smartphones, dash-cams, NSVs and aerial sources to capture road and asset data, then applies analytics to identify conditions, map assets and prioritise maintenance work. It displays results as GIS-based network maps with colour-coded segments, offers dashboards for budgeting, task assignment and performance tracking, and supports regular refreshes to monitor change over time.

Blyncsy Roadway Detections processes dash-cam imagery using image-analysis techniques to identify pavement issues (such as cracks and potholes), assess paint-line visibility, and generate condition scores across a roadway network. It supports segment-level rating (for example PASER analysis) and tracks degradation of striping and retroreflectivity over time. The results are delivered via mapped displays and dashboards, enabling users to visualise where maintenance is required and feed these insights into operational workflows.