---

name: geemap description: "Use geemap for Google Earth Engine interactive mapping, geospatial conversion, visualization, timelapse, and ML helper workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

geemap

Use this repo skill when the task involves geemap, the Python package for interactive geospatial analysis and visualization with Google Earth Engine (GEE). It covers map construction, Earth Engine layers, data conversion/export, charts and cartographic output, timelapse animations, app publication, local ML classifier conversion, and optional AI dataset-search helpers.

Start here

1. Confirm installation, Python version, Earth Engine credentials, and optional extras with references/installation-and-auth.md.
2. Run the safe package smoke helper when the user asks you to check an environment: scripts/check_geemap_env.py.
3. Pick the narrowest sub-skill below. If a workflow spans several areas, start with the primary output and follow the cross-links.
4. For repository freshness or refresh decisions, read references/repo-provenance.md.
5. For cross-cutting failures before diving into a workflow-specific page, read references/troubleshooting.md.

Route map

User intent	Read
Create a geemap.Map, choose ipyleaflet vs folium, add Earth Engine layers, basemaps, WMS/XYZ/COG/STAC/local raster layers, drawing tools, inspectors, layer managers, or save/embed an interactive map	sub-skills/interactive-earth-engine-maps/SKILL.md
Convert Earth Engine JavaScript to Python/notebooks, convert local vector/raster formats, export EE Images/FeatureCollections/videos, use COG/STAC/titiler helpers, or diagnose CRS/selectors/export tasks	sub-skills/conversion-and-io/SKILL.md
Create charts, colorbars, palettes, legends, cartoee/cartopy static maps, Plotly maps, pydeck, kepler.gl, or MapLibre visualizations	sub-skills/visualization-and-charts/SKILL.md
Generate Landsat/Sentinel/GOES/NAIP/MODIS/Dynamic World timelapses, annotate GIFs, convert GIF/MP4, publish maps to HTML/Streamlit/Gradio/Voila/Solara-style apps	sub-skills/timelapse-and-apps/SKILL.md
Convert local scikit-learn trees/random forests into Earth Engine classifiers, plan EE classification workflows, or use optional Gemini/AI dataset-discovery helpers	sub-skills/machine-learning-and-ai/SKILL.md

Minimal setup pattern

pip install geemap
python - <<'PY'
import geemap
print(geemap.__version__)
Map = geemap.Map(center=[40, -100], zoom=4, ee_initialize=False)
print(type(Map).__name__)
PY

Use ee_initialize=False for offline map construction or tests that should not contact Earth Engine. For real GEE layers or exports, authenticate and initialize Earth Engine first; see installation and auth.

Optional extras decision points

• Base map, conversion, local helper, and many tests use the base package.
• geemap[backends] adds pydeck and kepler.gl routes.
• geemap[raster], geemap[vector], and geemap[sql] add local raster, GeoPandas/OSM, and PostGIS-style workflows.
• geemap[apps] adds app-publication targets.
• geemap[ai] adds Gemini/LangChain/Google Cloud AI dataset-search routes.
• Avoid geemap[all] unless the user truly needs broad optional coverage; many extras bring compiled geospatial dependencies, services, credentials, or UI packages.

Safety and execution boundaries

• Do not run Earth Engine exports, notebook corpora, OSM/network downloads, cloud storage jobs, AI service calls, or app deployments unless the user explicitly wants those side effects and has credentials/network available.
• Prefer safe helper checks and dry-run planning scripts first; launch remote tasks only after validating ROI, scale, CRS, selectors, output path, and destination.
• Keep generated answers self-contained. Use this skill tree's references and scripts instead of depending on a repository checkout.
• CUDA/ROCm/MPS are not required for geemap itself; the important runtime gates are Python/Jupyter widgets, Earth Engine authentication, network access, and optional geospatial/service dependencies.