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User bases

People meet fAIr at three levels. Each one draws from the same model collection and feeds something back, so the work keeps improving.

The three fAIr roles: a mapper who picks a model and runs it, an advanced user who retrains it, and a developer who writes brand-new models.

Mapper

"There is a model out there. Let me see how it works in my area."

A mapper runs an existing model and reviews what it finds.

The fAIr interface running a swimming-pool detection model over aerial imagery, with a live prediction.

  1. Sees a model. Browses the collection for one that fits: buildings, trees, swimming pools, solid waste, and more.
  2. Runs it. Points the model at their own area and lets it detect features.
  3. Gets results. Predictions come back as GeoJSON, ready for the map.
  4. Validates. Checks each result. Feedback improves the model.

Validation can happen by hand, in the field, or at scale; see How it works.

Need a model for a different feature? Ask the team in #fair-coord on Slack.

Advanced user

"This is good, but tuned to my area it would work better."

An advanced user is a mapper who retrains a model for their own area and publishes the local version.

The fAIr training-area screen, where an advanced user sets up an area and maps example data to fine-tune a model.

  1. Picks a model. Starts from one already in the collection.
  2. Creates a training area. Sets up an area and maps example features there.
  3. Maps it and retrains. Feeds the local examples back into the model.
  4. Publishes. The tuned local version returns to the collection for the next mapper.

The full step-by-step is in Using fAIr, and Register a base model covers publishing.

Developer

"I built a model that maps a new feature. I want to share it."

A developer writes a new geo-AI model and adds it to fAIr.

  1. Writes the model. Builds and trains a new model, for example one that maps a feature not yet covered.
  2. Adds it to fAIr. Publishes the model to the collection with a clear name and the features it detects.
  3. Available to everyone. From that point, any mapper or advanced user can run it.

Models are contributed through a pull request to the fAIr-models catalog; see Models for what to contribute and ML pipeline for how registration works.