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olmoearth_inferenceX is a tool for auditing Earth-observation prediction maps without labels. It ranks the windows of a map by the model's own confidence, so a reviewer knows where to look first, says why each window is flagged, and measures how two inferences of the same scene differ.

One scene through the audit: Sentinel-2 bands, the frozen OlmoEarth encoder and the task head, the prediction, confidence and boundary layers, the review set at a 5% budget drawn on the scene, and the reasons per flagged window

On the 24 tasks of Ai2's published embedding suite where a confidence margin is defined, the model's own confidence ranks its errors better than any control that sees no model, on every one.

Full documentation is available at https://olmoearth-inferencex.readthedocs.io/: the findings, the usage, the recipe and the evidence experiment by experiment.

Demo

pip install olmoearth-inferencex
oe-inferencex demo

It audits a real map, one tile of Dynamic World land cover, chosen by a rule fixed in advance (the median tile of 18, not the best one), and draws this:

Three panels of a real land-cover map in southern Peru. Left: the 5% of windows to check first, outlined in black along the class boundaries. Middle: the same windows over the map's real errors in red. Right: a random 5% of windows over the same errors

Left: the 5% of windows to check first, found without labels. Middle: the same windows over the real errors, in red: 67% of them are wrong, against 19% of windows picked at random. Right: a random 5%. Most of the red lies outside the flagged windows because the map is 19% wrong and the review is 5%: no 5% could hold more than 26% of the errors, and these hold 17%; a 20% review finds 55%.

Your own map: oe-inferencex assess your_map.tif --out audit.

Setup

pip install olmoearth-inferencex        # add [geo] to read and write GeoTIFFs

To work on the repository, clone it, then uv sync and uv run pytest; the experiments also need uv sync --extra encoder --extra geo.

Apache License 2.0, see LICENSE; to cite, see CITATION.cff. For questions and suggestions, please open an issue on GitHub.

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