Viso.ai

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.

RoadAI uses a smartphone to survey road networks, capturing video while driving and applying computer vision to automatically identify surface distresses such as alligator cracks, potholes, settlement, and edge deterioration. It also detects roadside assets including traffic signs, line markings, guard rails, street lights, manholes, and drains. The system provides interactive maps and exportable data formats such as Excel, CSV, GeoJSON, and Shapefile for integration with asset management workflows.

PULSE for Pavement from CITYROVER uses a single vehicle-mounted camera to automatically collect data on roadways, then generates ratings, roughness scores, marking inventories and pavement failure lists. It delivers outputs such as PCI scores, IRI measurements, crack surveys and marking conditions without requiring manual inspection of each segment. The system is designed to support public-sector road planning by feeding the data into budgeting, lifecycle analysis, work planning and communication workflows.