geeViz
The Earth Engine Visualization Toolkit for Python
geeViz makes exploring, visualizing, and analyzing Earth Engine data and geospatial imagery easy in Python. Whether you’re an analyst, scientist, or just getting started, geeViz offers interactive mapping, time series, and advanced charting—without the JavaScript overhead.
Developed by RedCastle Resources, geeViz features a powerful, customizable map viewer and Pythonic interfaces for working with Google Earth Engine (GEE).
Key Features
- Interactive Map Viewer (launches in your browser)
- Layer toggling, opacity, visualization tools, querying, & area charting
- Dynamic time-lapse creation from GEE
ImageCollections - Built-in charting & analysis tools (point/polygon, time series, area stats)
- Inline zonal summary & charting (
geeViz.outputLib.charts, formerlygeeViz.chartingLib) — run zonal stats and produce Plotly charts (time series, bar, grouped bar, donut, scatter, per-feature time series subplots, Sankey) directly in notebooks - Summary area retrieval (
geeViz.getSummaryAreasLib) — 15 functions returning filteredee.FeatureCollectionobjects for political boundaries, USFS units, census geographies, buildings, roads, and protected areas - Jupyter/Colab support and standalone scripting
- Supports Landsat, Sentinel-2, MODIS, LCMS, LCMAP, and more
- Extensive examples and ready-to-run wrappers
- Built-in MCP server for AI coding assistants (Cursor, Claude Code, VS Code Github Copilot, Windsurf, AntiGravity, etc...)
Quick Links
- PyPI: pypi.org/project/geeViz
- Docs/Home: geeviz.org
- Notebooks & Scripts:
examples/ - Community Repo: github.com/gee-community/geeViz
- Forest Service GitHub: code.fs.usda.gov/forest-service/geeViz
JavaScript Version & Related Links
Documentation & Help
- geeViz Documentation and API Reference
- See
examples/for Jupyter/Colab notebooks and scripts. - Need help? Email us at info@geeviz.org.
Installation
The fastest way to get started:
- Sign up for Google Earth Engine
- Install geeViz via pip:
pip install geeViz
- Authenticate your Google account with Earth Engine:
earthengine authenticate
Manual / Advanced Installation
-
Install the Earth Engine Python API if not present:
pip install earthengine-api
-
Clone this repository:
git clone https://github.com/gee-community/geeViz
-
Optionally, add or symlink the
geeVizfolder to your Python site-packages. -
To update to the latest version:
pip install geeViz --upgrade
or, if installed via Git:
git pull origin master
geeViz is also mirrored at code.fs.usda.gov/forest-service/geeViz.
Optional: API Keys for Google Maps & Gemini
Some geeViz features (googleMapsLib, outputLib.reports LLM narratives) require API keys from Google Cloud. These are optional — core GEE functionality works without them.
Gemini API Key (for AI-generated report narratives and image interpretation):
- Go to Google AI Studio
- Click Create API Key → select a project → copy the key
Google Maps Platform API Key (for geocoding, Street View, places, elevation, air quality, solar):
- Go to Google Cloud Console → select/create a project
- Navigate to APIs & Services → Credentials → Create Credentials → API Key
- (Recommended) Restrict the key to: Geocoding, Street View Static, Places (New), Elevation, Air Quality, Solar, Roads, Maps Static
Store your keys in a .env file in your geeViz package directory (alongside geeView.py):
# .env file in your geeViz package directory
GEMINI_API_KEY=your_gemini_api_key_here
GOOGLE_MAPS_PLATFORM_API_KEY=your_maps_platform_key_here
You can also set these as environment variables. The .env file is loaded automatically by googleMapsLib and outputLib.reports on import.
| Key | Used by | How to get |
|---|---|---|
GEMINI_API_KEY |
googleMapsLib.interpret_image(), googleMapsLib.label_streetview(), outputLib.reports LLM narratives |
Google AI Studio |
GOOGLE_MAPS_PLATFORM_API_KEY |
googleMapsLib.geocode(), streetview_*(), search_places(), get_elevation(), get_air_quality(), get_solar_insights(), etc. |
Google Cloud Console |
Optional pip extras:
pip install geeViz[gemini] # Adds google-genai for AI features
pip install geeViz[segmentation] # Adds torch + transformers for SegFormer
pip install geeViz[all] # Everything
AI-Assisted Development (MCP)
geeViz includes a built-in MCP (Model Context Protocol) server with 12 tools that give AI coding assistants live access to geeViz and Google Earth Engine. Instead of generating code from training data (which is often wrong or outdated), your AI assistant can look up real function signatures, read actual example scripts, execute and test code, inspect assets, export data, and more.
Works with Cursor, Claude Code, VS Code with GitHub Copilot, Windsurf, and any MCP-compatible client. The mcp SDK is included as a dependency — no extra install needed.
Quick setup
- Add a config file for your editor (see MCP Server docs):
{ "mcpServers": { "geeviz": { "command": "python", "args": ["-m", "geeViz.mcp.server"] } } }
- Copy the agent instructions from
geeViz/mcp/agent-instructions.mdinto your editor's instructions file (.github/copilot-instructions.md,.cursorrules,CLAUDE.md, or.windsurfrules).
What the MCP server can do
The 12 tools are organized into categories:
| Category | Tools |
|---|---|
| Code Execution | run_code — persistent REPL with ee, Map, gv, gil, sal, tl, rl, cl, gm, edwLib pre-loaded; save_session — export as .py or .ipynb |
| API Introspection | search_geeviz — unified search across function signatures, module members, reference dictionaries, and example scripts (AST-indexed, zero import cost) |
| Dataset Discovery | search_datasets — keyword search across official & community catalogs |
| Asset Inspection | inspect_asset — bands, CRS, scale, date range, properties |
| Map Control | map_control — view, list layers, or clear the interactive map |
| Exports & Asset Management | export_image — to asset / Drive / Cloud Storage; manage_asset — delete / copy / move / create / update ACL |
| Google Maps | get_streetview — Street View imagery; geeviz_search_places — places / geocoding |
| Environment | env_info — versions, namespace, project info; view_output — open generated files (charts, thumbs, reports, HTML) |
Charting (cl.summarize_and_chart()), thumbnails (tl.generate_thumbs()), report generation (rl.*), EDW queries (edwLib.*), and geocoding (gm.geocode()) are accessed via run_code — one execution primitive plus a rich pre-loaded namespace beats a proliferation of narrow wrappers.
For the complete tool reference, architecture details, and usage examples, see the MCP Server README and the online MCP Server guide.
Getting Started
geeViz comes with ready-to-run examples and templates for fast onboarding.
Use with Jupyter & Colab
- Interactive notebooks are in the
examples/directory (see docs ➔). - The geeViz map viewer also works directly inside Jupyter and Google Colab.
Features at a Glance
- One line mapping: Map any GEE image or collection instantly
- Interactive: Toggle layers, set opacity, area/point query, and chart
- Dynamic Time-Lapses: Animate temporal stacks and export GIFs
- No JavaScript required: Pure Python interface
Contributing
We love contributions and new users!
- Share a GEE script, notebook, or suggestion?
info@geeviz.org - Pull requests & feature requests:
github.com/gee-community/geeViz
License
geeViz is released under the Apache 2.0 License.
See the LICENSE file for details.
1 maintainer
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