In a lot of Canadian cities, AI is starting to show up in places like traffic management and public safety. You’ll see computer vision being tested at intersections or used to spot risky pedestrian behavior. But these systems don’t just work out of the box, they need training data that matches what’s actually happening on the streets.
That’s why the annotation step matters so much. If you’re labeling footage from a snow-covered crosswalk or trying to identify cyclists through fog, the details really matter. What’s normal in Montreal might be totally different in Vancouver. Even things like bilingual signage can confuse a model if the data isn’t clear.
For teams working on smart city image annotation projects, that early annotation work is what sets the tone for everything that comes after. It doesn’t just help with accuracy, it helps make sure the system works for real people, in real places. That part often gets overlooked, but it’s where trust starts.
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