Introduction to Python Notebooks for working with Google Earth Engine (GEE)
In order to complete the lessons, you will need:
GitHub Account - for managing your own notebooks and container builds
Google Earth Engine Account - required to access Earth Engine API from notebooks
CyVerse Account - for running large VMs in CyVerse Discovery Environment
Install Docker - Install Docker on your own machines so you can run the containers we're going to use locally.
CodeSpaces Account- good for launching virtual development environments on cloud, mainly used for container development. requires a GitHub account and credit card (or be added to an educational account).
Official Documentation - documentation on using GEE
code.earthengine.google.com - work in the original browser based code editor (JavaScript).
EarthEngine Apps - brose published Apps from GEE
Qiusheng Wu is an assistant professor at University of Tennessee. He is the leading advocate for GEE and has authored many software packages, tools, and applications for the platform.
geemap - Python environment (packages) for working with GEE
Jupyter Notebooks and GEE - over 300 Jupyter Notebooks for using GEE
Samapriya's Awesome Community Datasets
EarthLab Intro to Python and GEE
Qiusheng has released a geospatial package for GEE, which is very useful.
We have a second, expanded version with a few more packages.
To deploy the environment in a Jupyterlab, first buid the conda environment in Terminal. Note, we're using mamba which is packaged with Jupyter Lab and is faster
mamba env create -f environment.yml
After the environment creates, activate it from Terminal
conda activate geospatial
To add the kernel to a notebook environment in Jupyter, use the Terminal
source activate geospatial
python -m ipykernel install --user --name geospatial --display-name "Python (Geospatial)"