Google Earth Engine Python API Quickstart

Expanding Your Earth Engine Skills with Google Cloud Platform

Overview

This workshop introduces the Google Earth Engine Python API as the next step for Stanford researchers and instructors who want to move beyond the JavaScript Code Editor and work with Earth Engine in Python, Jupyter notebooks, and cloud-based analysis environments.

A central focus of the session is understanding how Earth Engine integrates with Google Cloud Platform (GCP), including the use of user-managed cloud projects that enable Python API access, scalable computation, and reproducible workflows.

Workshop Description

This session walks participants through the full setup and use of the Earth Engine Python API, starting with cloud project configuration and ending with a complete, working analytical example in a Jupyter Notebook.

Participants will be guided through:

• Logging into the Earth Engine
• Creating a Google Cloud Project
• Registering the project as non-commercial
• Enabling the Earth Engine API within the project
• Attaching the project to an existing Earth Engine account

Once this process is complete, participants will be able to use the Earth Engine Python API, which was previously unavailable to student users.

Example Notebook and Workflow

The workshop is built around a hands-on walkthrough of an example Jupyter Notebook that demonstrates an end-to-end Earth Engine Python workflow. The notebook illustrates how to:

• Authenticate Earth Engine within a notebook environment
• Query and filter Earth Engine image collections
• Perform server-side analysis from Python
• Return and visualize results interactively

Introducing geemap

The workshop also introduces geemap, a Python package that simplifies interactive mapping, visualization, and export of Earth Engine data in Jupyter and Colab environments. geemap bridges Earth Engine’s server-side model with familiar Python-based exploratory and presentation workflows.

Topics Covered

• Earth Engine Python API fundamentals
• Earth Engine and Google Cloud project integration
• Authentication and project selection
• Client-side vs. server-side execution in Python
• Interactive mapping and visualization with geemap
• Patterns for reproducible, notebook-based workflows

Prerequisites

• Basic familiarity with Google Earth Engine concepts
• Working knowledge of Python
• Some exposure to GIS or remote sensing concepts

This workshop is not an introduction to Earth Engine or Python, but it is designed to be accessible to users who are still early in their Python-based geospatial work.

Testing/Getting Access

To determine whether you currently have access to the Stanford Google Earth Engine Org, as well as the ability to create GCP Projects for Google Earth Engine, you can do the following:

If you are a member of the Stanford Doerr School of Sustainability, you may already be a member of the GIS Users Workgroup, which gives you login access to Google Earth Engine, as well as Google Cloud Project creation, when using Earth Engine.

You can test your access to Google Earth Engine by logging in with your SUNetID Credentials, at the Earth Engine Code Editor: https://code.earthengine.google.com/

You will be guided through a process to create a GCP Project, register your project as a non-commercial project, as well as enabling the Google Earth Engine API, before you can then select the project for use in Google Earth Engine. Once you have gone through the process and attached the project to your Google Earth Engine account, you should be able to access the Earth Engine Python API.

Use the following to authenticate and see basic usage of the Python API: https://colab.research.google.com/github/google/earthengine-community/blob/master/guides/linked/ee-api-colab-setup.ipynb

If you find that your Stanford credentials do not allow you access to the Earth Engine platform, email Stace maples@stanford.edu