Apollo RoadSight

AI-powered road monitoring for automated asset inspection

Vehicle-mounted road inspection with AI-detected traffic sign labeled blocked by vegetation

Road asset inspection and maintenance are often labour-intensive, time-consuming, and reactive. City teams rely on manual audits, visual inspections, resident reports, and outdated imagery, limiting visibility into current road issues and maintenance priorities.

Apollo RoadSight is a road monitoring solution that automates inspection, asset tracking, and structured reporting for roadway infrastructure. Using updated field data and AI-assisted analysis, it helps municipalities monitor road assets more efficiently, optimize resources, and support proactive maintenance planning.

Road Intelligence & Asset Monitoring

AI-driven analysis of road networks for defect detection, asset identification, and geospatial mapping

Road Defects and Vegetation Monitoring

Detects and classifies pavement defects such as cracks, potholes, surface degradation and vegetation growth along roadway assets

Traffic Sign Detection & Inventory

Automatically identifies, classifies, and maps traffic signs with condition and type attributes

Pavement Marking Assessment

Evaluates lane markings, visibility, and wear for compliance and maintenance planning

Geospatial Defect Mapping

GPS-stamped detection of assets and defects for seamless GIS integration and tracking

Automated Asset Identification

Extracts and catalogs road assets such as catch basins, hydrants, and street infrastructure

Scalable Road Monitoring Workflow

Processes continuous video data across large networks for consistent, repeatable inspections

GoPro camera used for vehicle-mounted road monitoring

Vehicle-mounted road monitoring sensor layout with camera, GPS, and LiDAR sensors

Built for Network-Wide Road Visibility and Control

Road monitoring dashboard map showing geospatial defect detection points across a network

How Apollo RoadSight Works

Capture road data continuously using vehicle-mounted cameras, GPS, and LiDAR-assisted sensing during live patrols
Process and analyze data in the cloud using AI to identify road conditions, assets, and infrastructure elements
Detect and classify defects across issues like cracks, potholes, pavement wear, signage condition, vegetation, and pavement markings
Generate structured outputs with GPS-stamped insights for maintenance planning, reporting, and GIS integration