olmoearth_inferenceX

Checks an Earth-observation map after inference: where it is likely wrong, how wrong it is, which part you can trust, and which of two maps is better.

It reads only the per-class scores a model writes out. It never runs the model and needs no torch, so it works on OlmoEarth's outputs, on other models' maps and on published products with a confidence layer, whatever the imagery type: Sentinel-1, Sentinel-2, Landsat, or other satellites. (Tested so far on maps made from Sentinel-1, Sentinel-2 and Landsat.)

Documentation: https://olmoearth-inferencex.readthedocs.io/

Install

pip install "olmoearth-inferencex[geo]"    # Python 3.11 to 3.13; without [geo] it reads .npy only
oe-inferencex demo                          # a real Dynamic World tile; needs no data

What it answers

Every command starts with oe-inferencex. The input is the model's per-class scores before the argmax, as a GeoTIFF or a .npy array of shape (C, H, W). plan says how many labels a question needs and --help lists each command's options. The quick start runs each one on a test map.

Examples

Where do I look first? Dynamic World, a land-cover map made from Sentinel-2, on one tile in southern Peru. No labels are used to pick the windows; the expert's labels only show how it did.

The 5% it flags first are wrong 67% of the time. A random 5% would be wrong 19% of the time.

Where do two maps differ? A flood at Lake Shkodër, Albania, in March 2018 (Copernicus EMS activation EMSR273), seen by Sentinel-2 and Sentinel-1.

Inside the orange box, water mapped from each sensor before and after (imagery from GEOID-Flood and WorldFloods v2); orange squares are where two maps disagree:

Which of two maps is more accurate? Fields of the World's global field map (FTW, 2nd edition) beside a second model, PRUE B3, on a test chip of the FTW benchmark in Croatia.

Across Croatia's 353 test chips the two maps differ at 13% of windows. There FTW agrees with the benchmark 74% of the time, and labelling 100 of those windows names FTW the better map 99.9% of the time. In Belgium and Sweden, B3 was the better one.

Imagery: contains modified Copernicus Sentinel data (2017 to 2023). Dynamic World map and expert labels: Brown et al., Scientific Data, 2022, CC BY 4.0. FTW map: Fields of the World, CC BY 4.0. How the figures were made is in docs/figures/examples.

Limits

• Without labels it tells you where to look, not how wrong the map is.
• In a new place or year the ranking can weaken. Check it with a few labels.
• When two legends draw a class boundary differently (grass or shrub, say), only labels find those errors.
• Intervals and certified zones assume the labels are right.
• If parts of the map were made from different inputs (clouds, a missing sensor), use --condition.
• Labelling more and rerunning counts as a new test, unless you use a sequential sample.

Use from an agent

oe-inferencex mcp is a local MCP server; nothing is hosted. Add it to any MCP client as the command uvx --from "olmoearth-inferencex[geo,mcp]" oe-inferencex mcp, for example in Claude Code:

claude mcp add --scope user oe-inferencex -- uvx --from "olmoearth-inferencex[geo,mcp]" oe-inferencex mcp

Use a capable model; smaller ones dropped the limits the tools state. To try it, write the demo tile into an empty folder, start the agent there and ask:

uvx --from olmoearth-inferencex oe-inferencex demo

Where should I look first in oe_inferencex_demo/sample_probabilities.npy? Use windows of 1 pixel, and grade the order against oe_inferencex_demo/sample_truth.npy.

More

• Every result, its limits and the technical report: the research record.
• Development: clone the repository, then uv sync and uv run pytest.
• Apache License 2.0 (LICENSE); cite with CITATION.cff; questions in GitHub issues.