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name: machine-learning-and-ai description: "Use geemap machine learning conversion helpers and optional AI dataset discovery interfaces." disable-model-invocation: true metadata: disco-role: operating license: MIT

geemap Machine Learning and AI

Use this sub-skill when a task involves geemap's Earth Engine classification workflows, local scikit-learn decision-tree/random-forest conversion into Earth Engine classifier strings, classifier CSV or FeatureCollection persistence, or optional AI-assisted Earth Engine dataset discovery.

Start here

1. Confirm basic geemap, Earth Engine, and optional dependency setup with ../../references/installation-and-auth.md.
2. For local model conversion, use references/workflows.md and the bundled scripts/rf_tree_smoke.py.
3. For exact function signatures, inputs, outputs, and gotchas, use references/api-reference.md.
4. For error handling, use references/troubleshooting.md.

What this sub-skill owns

• geemap.ml.tree_to_string for one fitted scikit-learn decision tree.
• geemap.ml.rf_to_strings for fitted scikit-learn random-forest or ExtraTrees-style ensembles.
• geemap.ml.strings_to_classifier for in-memory Earth Engine classifier construction from tree strings.
• geemap.ml.trees_to_csv and geemap.ml.csv_to_classifier for local CSV persistence and reload.
• geemap.ml.export_trees_to_fc and geemap.ml.fc_to_classifier for Earth Engine asset FeatureCollection persistence and reload.
• Earth Engine supervised/unsupervised classification workflow structure: sample training data, train or import a classifier/clusterer, classify an image, then route display or accuracy work to the sibling sub-skills.
• Optional geemap.ai dataset catalog, Gemini/Genie, embedding, and dataset explorer surfaces when the ai extra, Google credentials, network access, and notebook UI are available.

Route elsewhere

• Generic file conversion, Earth Engine export tasks, shapefile/GeoJSON/CSV data movement, or non-classifier table export: ../conversion-and-io/SKILL.md.
• Adding classified images or vector layers to an interactive map: ../interactive-earth-engine-maps/SKILL.md.
• Accuracy matrices, charts, legends, palettes, static cartography, or chart rendering: ../visualization-and-charts/SKILL.md.
• Timelapse, GIF, app, or publication workflows: ../timelapse-and-apps/SKILL.md.

Operating constraints

• Local tree conversion is concrete and deterministic, but it requires scikit-learn, numpy, pandas, and a fitted compatible estimator.
• Earth Engine classifier objects can be constructed locally from strings, CSV, or FeatureCollections, but using them to classify images or export assets requires Earth Engine initialization, credentials, a project when required by the Earth Engine client, and network access.
• AI helpers are optional, credentialed, and networked. Treat them as convenience interfaces, not required geemap ML functionality.
• Do not rely on source notebooks or repository checkout files at runtime; the self-contained recipes and script in this sub-skill are the supported reference.