Explore how smart cities are using AI graffiti detection systems and computer vision to improve public space maintenance by quickly addressing graffiti issues.
Graffiti, which consists of writing, drawings, or symbols, can tell many stories. It adds color and character to city walls and is used to express opinions. While sometimes graffiti is seen as art, more often it is done without permission. In fact, graffiti frequently shows up overnight on buildings, bridges, and trains, and cleaning it up takes time, resources, and effort.
Cities around the world spend millions trying to keep public spaces clean. In the United States alone, graffiti cleanup costs more than $12 billion each year. Also, in many cities, staying ahead of graffiti is a substantial challenge. There are only so many cleanup crews, and they can’t be everywhere at once.
That’s why city planners are now turning to computer vision, a branch of artificial intelligence that enables machines to analyze and interpret images. Using computer vision models like Ultralytics YOLO11, AI-enabled cameras can be used to quickly scan city walls, detect graffiti in real time, and pinpoint the exact locations for teams to respond.
In this article, we’ll take a closer look at how computer vision is being used for automated graffiti recognition in smart cities. Let’s get started!
Smart cities are using cutting-edge technology to handle everyday challenges more efficiently. Specifically, they are leveraging computer vision models like YOLO11 to analyze visual data, monitor public spaces, and support faster, data-driven decision-making across various urban systems.
Here are some common ways in which Vision AI is being used in smart cities:
Now that we’ve explored how computer vision is helping make cities smarter, let’s take a closer look at why it’s especially needed for automated graffiti recognition.
Traditionally, cities have used a range of approaches to manage graffiti. Some rely on public reports, while others send teams to inspect known hotspots. In certain cases, mobile apps allow residents to submit complaints or upload images. While these methods can be helpful, they are time-consuming, rely heavily on human input, and often can’t provide full coverage across the city.
Computer vision offers cities a faster and more reliable way to manage graffiti. Automated graffiti recognition systems typically rely on two main computer vision tasks: image classification and object detection.
In the first step, image classification analyzes a photo to determine whether it shows graffiti, street art, or a clean surface. If graffiti is found, object detection pinpoints its exact location in the image. This enables city teams to respond quickly and accurately. Over time, the data helps identify trends so that cities can target frequent hotspots and improve prevention efforts.
Many cities are trying out new ways to handle graffiti. Instead of just depending on patrols or public reports, they’re starting to use computer vision models that can spot, identify, and even help clean up graffiti. Let’s take a look at how some cities are putting these new tools to use.
Public service vehicles are now being used in many cities to help detect graffiti during their regular patrol routes. By equipping these vehicles with computer vision solutions, cities can scan streets in real time, without the need for extra patrols. This makes it easier to catch graffiti early and send alerts to cleanup crews before the problem spreads.
Tempe, Arizona, is a great example of a city using this technology. The city partnered with local organizations to develop a system using cameras mounted on public service vehicles. As these vehicles moved through neighborhoods, they captured images of walls and public spaces.
A Vision AI model analyzed the images and flagged any graffiti it detected. The results were sent to a live dashboard so management could check updates on their phones and respond quickly. This helped Tempe act faster and reduce public complaints.
Similarly, in Lisbon, the capital of Portugal, a proof-of-concept system was developed using cameras mounted on city vehicles to automatically detect graffiti.
Graffiti can appear in places that are difficult or unsafe to reach, like overpasses, tunnels, and elevated structures. Cleanup in these areas usually requires extra equipment, added cost, and more time. To improve safety and speed, some cities are starting to use drones that can both detect and remove graffiti without the need for manual crews on site.
For instance, Seattle, a major city in Washington State, recently launched a drone-based solution to monitor areas that are hard to reach. The drones are equipped with cameras and spray tools. They can scan public surfaces for markings, and when graffiti is detected, they use a built-in sprayer to paint over it and cover it up.
Seattle’s graffiti detection efforts also include solar-powered cameras installed in known graffiti hotspots. These cameras monitor activity and send alerts when someone approaches a tagged location. By combining drones with fixed monitoring points, Seattle is able to respond more quickly to graffiti and reduce physical risks for maintenance crews.
In some cases, graffiti detection isn't just about cleanup - it's also about understanding such activity patterns across the city. With the help of AI, cities can now turn street-level images into visual maps that show where graffiti happens most often. These maps reveal trends that may not be obvious through public reports and help city planners focus their efforts where they're needed most.
For example, São Paulo, a city in Brazil, explored this approach through a research-driven case study. A team used computer vision to analyze over 275,000 street-level images from across the city, collected using Google Street View. Graffiti identified in these images was mapped across 96 districts, revealing where graffiti was most concentrated.
Instead of relying on scattered reports or occasional inspections, this approach provided a large-scale, data-driven view of graffiti across the city. The resulting map gave São Paulo a valuable resource for tracking changes over time and supporting more targeted cleanup, prevention, and urban planning efforts.
Here are some key benefits of using computer vision for graffiti management:
While computer vision systems are useful for graffiti detection, they also have a few limitations. Here are some things to keep in mind when putting such systems in place:
Graffiti detection is just one example of how computer vision is transforming the way cities manage public spaces. These systems are increasingly being integrated with tools for maintenance, reporting, and city planning.
The focus is shifting from quick reactions to proactive awareness. With better data and smarter tools, cities are moving toward preventing problems before they start and making more informed decisions every step of the way.
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