Neural Networks in the Moscow Region Detect More Than 3,000 Road Defects
In Russia’s Moscow Region, artificial intelligence is being used to monitor road conditions at scale. During a pilot rollout, an AI-based system automatically identified more than 3,000 defects in asphalt pavement, including potholes, surface failures, and structural damage.
Every Meter Matters
Across the region, 40 mobile monitoring units equipped with neural network software are now operating on service vehicles. These systems analyze video streams in real time, automatically detecting road defects and linking each issue to precise geocoordinates with meter-level accuracy. The data is transmitted instantly to road maintenance contractors for corrective action. The main advantage of this approach is a shift from a reactive model – waiting for citizen complaints – to a proactive one, where problems are detected and fixed before drivers notice them. This change alone has reduced the volume of resident complaints by roughly 10%.
In 2023, a similar pilot project was launched in the Novosibirsk Region, where buses were equipped with AI-powered video cameras to detect trash and potholes on roads. The Moscow Region, however, has accumulated the most extensive experience in deploying smart cameras and neural networks to monitor the urban environment. The total number of video cameras integrated into the region’s unified network has exceeded 160,000, with more than 93,000 operating on neural network technologies. Against this backdrop, scaling up AI-based road condition monitoring is largely a matter of time.
Integration Into a Digital Ecosystem
The system is integrated with regional digital platforms such as Dobrodel, creating a unified framework for infrastructure management. Citizen complaints and AI-generated alerts enter the same tracking and oversight system, eliminating duplication and enabling an objective assessment of road service performance. Defects are identified at an early stage, often before they develop into serious potholes. This lowers repair costs and improves road safety, delivering tangible benefits for both municipal budgets and everyday road users.
The technology could become part of a broader national strategy for the digitalization of urban infrastructure. Further development is expected to focus on integration with repair planning, budgeting, and logistics systems. This would enable a transition to predictive road maintenance based on wear forecasts rather than emergency repairs
Toward a Digital Standard
The outcome of the deployment is not limited to the 3,000 defects detected in a short period. It also reflects the emergence of a new, data-driven management culture. The trend toward using AI in public administration – from road maintenance to parking control and waste collection – is gaining momentum.
In the coming years, similar AI-based monitoring systems are expected to become standard practice across Russia’s major regions. Over time, fragmented solutions are likely to be replaced by comprehensive intelligent platforms for road management. These systems would combine real-time data from cameras, sensors, digital maps, and predictive models, enabling authorities not just to patch potholes, but to maintain the country’s transport infrastructure in a smarter and more cost-effective way.
