Visakhapatnam is Andhra Pradesh's largest city and one of India's most significant port hubs — a coastal metropolis whose road network underpins daily mobility for millions of residents, commuters, and freight operators. The Greater Visakhapatnam Municipal Corporation (GVMC) is responsible for the upkeep of this network, but like most Indian urban bodies, it was managing a vast infrastructure challenge with limited visibility.
Traditional inspection methods were time-consuming and labour-intensive. Field teams conducted periodic visual surveys that produced inconsistent, non-comparable data. There was no standardised baseline, no GPS-tagged defect inventory, and no way to objectively prioritise which corridors needed urgent intervention versus routine maintenance.
The result was a reactive maintenance cycle — responding to complaints, repeating repairs on the same stretches, and having no mechanism to verify whether contractor work had actually improved road conditions. GVMC needed a modern, data-driven system — powered by AI-based road condition monitoring — that could change this at scale.
GVMC partnered with RoadVision AI to conduct a pilot project covering 42.54 kilometres of roads across the city's key corridors. The mandate was clear: demonstrate AI-powered pavement inspection, identify defects at scale, generate actionable insights, and deliver outputs that GVMC's engineering teams could immediately act on.
Survey vehicles fitted with the RoadVision AI data collection app and a suction windshield mount drove Visakhapatnam's roads at regular traffic speed. No specialist equipment. No lane closures. No disruption to daily movement. The app captured high-resolution video, images, and GPS coordinates continuously throughout the survey drive, demonstrating the effectiveness of mobile road survey technology for large urban networks.
Collected data was processed through RoadVision AI's intelligence platform, which automatically identified and classified road types, conditions, and distresses in line with IRC guidelines. The platform calculated pavement distress ratings, detected road signage compliance, and built digital twins of the surveyed corridors — virtual replicas capturing real-world conditions with full spatial accuracy.
Every defect was geospatially pinned. Every road segment received an IRC condition score. The full output was available on a cloud-hosted GIS platform with color-coded severity mapping — transforming the survey results into a data-driven road asset management system for GVMC planners and engineers.
The pilot revealed a road network under significant stress. Across 42.54 kilometres of assessed corridors, the AI engine detected and catalogued 3,955 individual defects — classified by type, severity, and precise GPS location. Ravelling dominated the distress profile, pointing to a systemic pattern of surface layer deterioration that accelerates into structural failure if left unaddressed.
Key corridors assessed included Sampath Vinayaka Temple Road, Waltair Station Approach Road, and Scindia Road — each receiving individual segment scores and distress breakdowns that enabled corridor-level prioritisation for the first time through automated road defect detection.
The pilot delivered three primary outputs, each designed to directly serve GVMC's engineering and planning workflows — replacing manual reports with structured, comparable, and immediately actionable intelligence.
The first was a set of AI-generated reports — detailed condition assessments and defect analyses per road segment, delivered in IRC-compliant PDF and CSV formats ready for engineering and planning teams to act on directly.
The second was a live GIS dashboard — an interactive, web-based platform visualising the full road network with color-coded segmentation by condition severity, accessible to planners, engineers, and accountability officers in real time.
The third was a road signage inventory — a complete record of compliant and missing signages across all assessed corridors per IRC standards, enabling targeted safety rectification without the need for a separate manual audit.
India's urban local bodies are under growing pressure to demonstrate infrastructure accountability — to state governments, to citizens, and to the audit bodies that review public works expenditure. The Visakhapatnam pilot shows exactly what is now possible: a 42.54 KM survey, 3,955 catalogued defects, IRC-rated segment scores, and a live GIS dashboard — all delivered through a smartphone-based system that requires no road closure, no specialist equipment, and no weeks of mobilisation.
GVMC now has something most Indian municipal corporations do not: a verifiable, timestamped baseline for its road network. That baseline is the foundation for vendor accountability, budget justification, and proactive maintenance planning that can sustain a city of Visakhapatnam's scale and ambition.
The model is replicable. The data is actionable. And the path to full-scale deployment is already open.
The Autonomous Road Engineer
AI that is transforming road infrastructure, from reactive to predictive.
