/answers/how-to-spot-litter-and-graffiti-with-computer-vision/index.md · 42 lines
---
title: "How to spot litter and graffiti with computer vision"
description: "Photos from a vehicle on a set route, or from fixed cameras aimed at walls, lots and grounds, feed a model trained on the owner own images. It marks likely litter, overflowing bins, dumped items and new graffiti, and tags each one with a place and a photo so the crew knows where to go and what to bring. Comparing one pass with the next shows which spots keep coming back, which helps plan bins, lighting and cleaning rounds. Aim cameras at surfaces and grounds rather than at people, take photos when areas are quiet, and blur any person or plate that still appears before photos are stored. A crew lead reviews the flags each day and decides what gets cleaned, painted over or reported. The model does not replace walk-throughs or resident reports, and people make every work order call."
url: https://lidvizion.ai/answers/how-to-spot-litter-and-graffiti-with-computer-vision/
site: Lid Vizion
index: https://lidvizion.ai/llms.txt
---
[Direct answers](https://lidvizion.ai/answers/)
# How can a property manager, campus or city crew use computer vision to find litter and graffiti faster?
Photos from a vehicle on a set route, or from fixed cameras aimed at walls, lots and grounds, feed a model trained on the owner own images. It marks likely litter, overflowing bins, dumped items and new graffiti, and tags each one with a place and a photo so the crew knows where to go and what to bring. Comparing one pass with the next shows which spots keep coming back, which helps plan bins, lighting and cleaning rounds. Aim cameras at surfaces and grounds rather than at people, take photos when areas are quiet, and blur any person or plate that still appears before photos are stored. A crew lead reviews the flags each day and decides what gets cleaned, painted over or reported. The model does not replace walk-throughs or resident reports, and people make every work order call.
## What this means in practice
- Start with one route or a few problem walls and lots.
- Label photos from your own site for litter, dumped items and graffiti.
- Aim cameras at surfaces, not people, and blur anyone who appears.
- Send each flag to the crew lead with a place and a photo.
- Compare passes to find repeat spots and plan bins and rounds.
## How Lid Vizion approaches it
We start with the business decision and operating context, define ownership and measurable milestones, then use configuration, integrations, automation, or custom software only where each earns its place.
## More on Automation & custom AI
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