Overstory

A machine learns to recognise a forest. Then it is drawn as one.

Overstory trains a random forest classifier — an ensemble of decision trees — to tell woodland from everything else, using nothing but the shape of a year of satellite greenness over each patch of ground. Forest and farmland green up differently, and the classifier learns that difference.

Then the metaphor is taken at its word. Each decision tree in the ensemble is drawn root-down as an actual tree, its branching the branching of its own logic, and the ensemble is scattered into a wood. The picture of the model and the thing the model recognises become the same image.

The satellite data is real and public. The classifier genuinely works. Every tree in the scene is one the model actually grew.

Works from this series are in production, and will appear here as they are finished.