Examples¶

This section provides comprehensive examples demonstrating how to use geeViz for various geospatial analysis tasks. Examples are organized by category and include both Jupyter notebooks and Python scripts.

Important

geeViz.geeView map outputs will not display correctly in the rendered notebooks below. They will display correctly when you run them in your own environment.

Python Notebook Examples¶

Getting Started¶

Introduction to geeViz’s core functionality and interactive mapping capabilities.

Starter geeViz geeView Notebook

Authentication & Multi-Tenant Proxy¶

End-to-end walkthrough of geeViz.eeAuth — the auth-proxy library that lets one Python process talk to Earth Engine on behalf of many service accounts / OAuth refresh tokens. Covers the zero-config path (what Map.view() does automatically), explicit addCreds registration, scoped switching with with eeCreds.use(name), the four GEEVIZ_EEAUTH_MODE lifecycles (detached is the default since 2026.6.1), embedding the proxy in your own FastAPI app, and running it as a standalone service.

geeViz.eeAuth — multi-credential Earth Engine in one Python process

Visualization Examples¶

Examples demonstrating how to visualize various Earth Engine datasets and products.

Image Processing & Analysis¶

Learn how to process and analyze satellite imagery using geeViz helper functions.

Create High Quality Landsat Composites

Charting, Thumbnails & Summary Areas¶

Zonal statistics, Plotly charts, Sankey diagrams, thumbnails, filmstrips, and summary area functions.

Reports¶

Automated report generation combining thumbnails, charts, Sankey diagrams, and LLM narratives.

geeViz Report Generation Examples

Google Maps Platform¶

Ground-truthing with Street View, geocoding, places, elevation, AI interpretation, and segmentation.

geeViz Google Maps Platform Examples

Weather Forecasts¶

Time-lapse visualization of GFS, ECMWF, and WeatherNext forecast models.

Global Weather Forecast Time Lapses with geeViz

Comparison with Other Tools¶

Understand the differences and advantages of geeViz compared to other Earth Engine Python packages.

A comparison between geeViz and geeMap’s map visualization capabilities

Change Detection Workflows¶

Examples showing how to run and visualize temporal change detection algorithms.

Pandas Integration¶

Convert Earth Engine data structures to pandas DataFrames.

Examples of how to work between GEE and Pandas

Python Script Examples¶

The following scripts can be run directly from the Python interpreter. These examples demonstrate various geeViz capabilities in standalone Python scripts:

# Basic visualization and time-lapse
import geeViz.examples.timeLapseExample
import geeViz.examples.geeViewExample

# Image collection processing
import geeViz.examples.getLandsatWrapper
import geeViz.examples.getSentinel2Wrapper
import geeViz.examples.getCombinedLandsatSentinel2Wrapper

# Time series analysis
import geeViz.examples.harmonicRegressionWrapper

# Change detection algorithms
import geeViz.examples.LANDTRENDRWrapper
import geeViz.examples.LANDTRENDRViz
import geeViz.examples.CCDCViz

# Dataset viewers
import geeViz.examples.lcmsViewerExample
import geeViz.examples.LCMAP_and_LCMS_Viewer

# Phenology and specialized tools
import geeViz.examples.phEEnoVizWrapper

# Weather forecasts
import geeViz.examples.GFSTimeLapse
import geeViz.examples.WeatherNextTimeLapse

# Report generation
import geeViz.examples.report_generation
import geeViz.examples.lolo_nf_fire_report

External Resources¶

Additional training materials and examples are available through the LCMS training repository:

Landscape Change Monitoring System Training Notebooks

This repository includes comprehensive tutorials developed by RedCastle Resources and Google for learning Earth Engine and geeViz workflows.