Kirov Uses AI to Monitor Municipal Contractors
In Kirov, artificial intelligence is helping detect violations of municipal improvement rules. Connected to video surveillance systems, it monitors excavation work.
A new module has been integrated into a mobile software system designed to document violations. The system can automatically identify excavation sites where barriers and warning lights are missing. It also tracks deadlines for restoring asphalt and lawns after repairs, as well as the removal of soil and debris from surrounding areas.
The AI is integrated with the city’s video surveillance system. It analyzes photos and video footage sent to servers and, when necessary, records the time, circumstances and coordinates of an incident.
According to the city administration, the system has already delivered initial results: its algorithm detected a violation of municipal improvement rules during excavation work on city-owned property. Businesses that commit such violations face fines ranging from 50,000 to 200,000 rubles (approximately $600 to $2,400).
For the Kirov city government, the system will make it possible to respond more quickly when contractors fail to follow work requirements. Residents, meanwhile, should encounter fewer unprotected excavation pits and damaged lawns.
AI Applications in Municipal Services Set to Expand
The system launched in Kirov has strong potential for adoption in other Russian cities. Problems arising from excavation work affect most municipalities.
The capabilities of these algorithms could also be expanded substantially. For example, the Moscow Urban Planning and Construction Complex’s Artificial Intelligence Center has developed a service that can determine whether excavation work is authorized with 90% accuracy. This reduces inspectors’ workloads by eliminating the need to check sites where all requirements have been met and the necessary permits are in place.
The center has already signed 29 agreements with 21 Russian regions to test and deploy its AI technologies. As a result, cities across Russia will have the opportunity to use neural-network analytics to address a broader range of municipal services and infrastructure issues.
From Road Potholes to Waste Container Sites
In recent years, Russian municipalities have increasingly used artificial intelligence algorithms across different areas of urban infrastructure. In St. Petersburg, for example, the Gorodovoy neural-network system has been operating since 2023. It analyzes data from cameras installed on municipal vehicles and identifies violations of city improvement rules, including graffiti, illegally installed air-conditioning units and road potholes.
In Novosibirsk, the FG-Nestbox-01 hardware and software system has been analyzing photo and video feeds from fixed and mobile cameras since September 2025. It detects deviations from established requirements for excavation work, problems with snow removal, graffiti-covered building facades and other issues.
In Tula, NtechLab, a Rostec partner, is testing an AI-powered video analytics system. It detects overflowing waste containers and missed deadlines for removing municipal solid waste, vehicles blocking access to waste container sites, dogs being walked in prohibited areas and other incidents.
Further Automation of Municipal Tasks
Kirov’s adoption of AI algorithms is an example of automating a specific municipal task. Drawing on experience from other Russian cities, the service’s capabilities could be expanded considerably. For example, the neural network could be integrated with permit registries and geographic information systems, following the Moscow model, to help agencies identify unauthorized excavations.
Cities and regions continually exchange experience. Neural-network platforms tested in Russia’s major urban centers are being rolled out in other federal subjects. Local developers have also delivered successful projects.
The digitalization of Russia’s municipal services is giving the IT sector a valuable boost, creating opportunities to improve existing products and develop new ones, with the prospect of exporting proven solutions.
The greatest value of these projects, however, lies not in using AI for its own sake, but in achieving measurable improvements to the urban environment.
