Google-earth-engine

Here is a free open access book including fundamentals and applications of Google Earth Engine. It suits users of all levels - from beginners to advanced users.

This blog post describes a method to utilize Google Earth Engine from within BigQuery's SQL allowing SQL speakers to get access to and value from data available within Earth Engine.

Here are some tools for working with Google Earth Engine from a Jupyter development environment which provide a foundation for new libraries on top of the Google Earth Engine Python API.

Setting up access to Google Earth Engine from public apps could be rather complicated. Here is a post outlining the steps needed to start using Google Earth Engine from a Streamlit application.

The geeml Python package makes it easier to extract satellite data from Google Earth Engine using parallel processing and the Google Earth Engine high volume endpoint.

Here is an Google Earth Engine guided project on land cover analysis. These video tutorials with open-source code show how to work with land cover data in Google Earth Engine.

Take a look here for some free resources on learning QGIS, Python, PyQGIS, Google Earth Engine and GDAL/OGR. You can pick courses that suit your current level of expertise.

The QGIS Earth Engine plugin integrates Google Earth Engine and QGIS using EE Python API. The user needs to have an active Google Earth Engine (EE) account to use the plugin. Learn more here.

This post shows you how to access public Google Earth Engine assets, view and analyze them, and save as a portable GeoTIFF format using only the terra R package.

Google Earth Engine and BigQuery are both tools on Google Cloud Platform that allow you to interpret, analyze, and visualize geospatial data. This post demonstrates how data can be moved to BigQuery.