How Cities Use AI and Vehicle Cameras for Road Maintenance

Municipal road maintenance departments are increasingly adopting vehicle-mounted cameras and computer vision artificial intelligence (AI) to automate the inspection of aging road networks. By attaching smart cameras to existing city fleets, municipalities can continuously scan pavement as vehicles drive their normal routes. The AI software processes this video footage to automatically detect, classify, and map road defects like potholes and alligator cracking. This article explores the step-by-step process of how this technology captures data, analyzes pavement health, and transforms how cities prioritize and execute road repairs.

Data Collection Using Existing City Fleets

Traditionally, road inspections required specialized survey vans or manual walk-throughs by human inspectors, which are both slow and expensive. Today, cities bypass this bottleneck by mounting standard high-definition cameras—or even specially configured smartphones—onto the windshields and dashboards of existing municipal vehicles.

Garbage trucks, street sweepers, code enforcement cars, and public transit buses are ideal for this task because they already traverse nearly every street in a city on a weekly or daily basis. As these vehicles go about their normal duties, the mounted cameras continuously record video or take rapid-fire still images of the pavement directly ahead. At the same time, integrated GPS sensors tag every frame with precise latitude and longitude coordinates, along with the vehicle’s speed and the time of capture.

How Computer Vision Detects Road Defects

Once the visual data is captured, computer vision AI takes over. Computer vision is a field of artificial intelligence that trains computers to interpret and understand the visual world. In road maintenance, the AI uses deep learning models—specifically Convolutional Neural Networks (CNNs)—that have been trained on hundreds of thousands of images of damaged asphalt.

As the AI scans the footage, it looks for specific pixel patterns, shadows, and textures that indicate structural failure:

  • Potholes: The AI identifies potholes by recognizing sharp, dark edges surrounding a bowl-shaped depression in the pavement. Using the known height and angle of the camera, advanced systems can also estimate the surface area and depth of the pothole.
  • Alligator Cracking: Also known as fatigue cracking, this defect looks like a series of interconnected cracks resembling the scales of an alligator. It is a sign of severe structural failure beneath the asphalt. The AI detects this by identifying dense, web-like clusters of dark lines on the road surface, differentiating them from harmless single linear cracks or tar sealants.

To avoid clogging the system with useless data, the AI is also trained to ignore non-issues that might confuse a basic camera, such as manhole covers, storm drains, roadkill, tree shadows, and oil stains.

Edge Computing vs. Cloud Processing

The processing of this visual data happens in one of two ways: at the "edge" or in the cloud.

In an edge computing setup, the camera device itself contains a powerful processor that runs the AI model in real time. As the vehicle drives, the device instantly analyzes the road, deletes the footage of healthy pavement, and only uploads images of confirmed potholes and cracking over a cellular network.

In a cloud-based setup, the camera simply records the data. When the municipal vehicle returns to the city yard at the end of its shift, it automatically connects to the city's Wi-Fi, uploads the raw footage to cloud servers, and the AI processes the data overnight.

Cataloging and Mapping the Damage

After the AI identifies a pothole or a patch of alligator cracking, the data is automatically organized and sent to the municipality’s Geographic Information System (GIS) or Pavement Management System (PMS).

Instead of receiving spreadsheets, maintenance supervisors look at an interactive digital map of the city. The software color-codes streets based on the Pavement Condition Index (PCI), a standard numerical rating from 0 to 100 that indicates the general health of a road segment.

Every detected defect is plotted on the map as a clickable pin. When a supervisor clicks a pin, a profile opens displaying:

  • A high-resolution photo of the pothole or alligator cracking.
  • A bounding box drawn by the AI highlighting the exact defect.
  • The severity level (typically categorized as low, medium, or high).
  • The exact GPS coordinates and street address.
  • Historical data showing if the defect has grown since the last time a camera passed by.

From Detection to Repair

The ultimate goal of cataloging this data is to optimize repair workflows. Before AI, cities relied heavily on reactive maintenance—waiting for citizens to call a hotline to report a pothole. This resulted in crews zig-zagging inefficiently across the city to fix isolated problems.

With an AI-generated catalog, maintenance departments can shift to proactive and preventative maintenance:

  1. Automated Work Orders: When the AI detects a high-severity pothole that poses a safety hazard, the system can automatically generate a work order and assign it to a repair crew.
  2. Route Optimization: Crews can see all the potholes in a specific neighborhood on a single tablet screen, allowing them to fix multiple defects in one trip rather than just the single pothole a citizen complained about.
  3. Preventative Maintenance: By tracking alligator cracking early, engineers know the sub-base of the road is failing. Instead of wasting money filling potholes that will quickly return on that street, they can schedule the road for a complete mill-and-overlay resurfacing before it deteriorates completely.

By turning routine city vehicle routes into automated inspection missions, computer vision AI allows municipalities to save money, extend the lifespan of their roads, and fix hazards before they damage citizens' vehicles.