Motivation

At Mozilla.ai, we believe that there are a lot of opportunities where artificial intelligence (AI) can empower communities driven by open collaboration. 

These opportunities need to be designed carefully, though, as many members of these communities (and people in general) are increasingly worried about the amount of AI slop flooding the internet.

With this idea in mind we developed and released the OpenStreetMap AI Helper Blueprint. If you love maps and are interested in training your own computer vision model, you’ll enjoy diving into this Blueprint.

Why OpenStreetMap?

Data is one of the most important components of any AI application, and OpenStreetMap has a vibrant community that collaborates to maintain and extend the most complete open map database available.

If you haven’t heard of it, OpenStreetMap is an open, editable map of the world created by a community of mappers who contribute and maintain data about roads, trails, cafés, railway stations, and more.

Combined with other sources, like satellite imagery, this database offers infinite possibilities to train different AI models.

As a long-time user and contributor to OpenStreetMap , I wanted to build an end-to-end application where a model is first trained with this data and then used to contribute back.

The idea is to use AI to speed up the slower parts of the mapping process (roaming around the map, drawing polygons) while keeping a human in the loop for the critical parts (verifying that the generated data is correct).

Why Computer Vision?

Large Language Models (LLM) and, more recently, Visual Language Models (VLM) are sucking all the oxygen out of the AI room, but there are a lot of interesting applications that don’t (need to) use this type of models.

Many of the Map Features you can find in OpenStreetMap are represented with a polygon (‘Area’). It turns out that finding and drawing these polygons is a very time consuming task for a human, but Computer Vision models can be easily trained for the task (when provided with enough data).

We chose to split the work of finding and drawing map features into 2 computer vision tasks using state-of-the-art non-LLM models:

• Object Detection with YOLOv11, by Ultralytics, which identifies where relevant features exist in an image.
• Segmentation with SAM2, by Meta, which refines the detected features by outlining their exact shape.

These models are lightweight, fast, and local-friendly – it’s refreshing to work with models that don’t demand a high-end GPU just to function. As an example, the combined weights of YOLOv11 and SAM2 take much less disk space (<250MB) than any of the smallest Visual Language Models available, like Sm