Our user community is constantly building novel and impactful applications of the Earth Engine platform. This space is dedicated to our prolific and talented users who want to share their own hands-on guides with the rest of the community. Created by Earth Engine users, for Earth Engine users, tutorials in this section are intended for all levels, from beginner guides to walk throughs of more advanced techniques. Community tutorials are stored in Markdown files on GitHub where they can be reviewed and edited by the community. Where your-tutorial-name is the short name of your tutorial in all lowercase.
The materials on this page are community developed curricula for teaching Earth Engine in higher education. Scroll the directories below to see all the contributed content. If you use these materials to develop courses with Earth Engine, please give attribution! To contribute teaching materials to this page, contact email@example.com. Some Code Labs have been translated to Japanese by Yu Fujimoto of Nara University, Nara City, Japan.
Training and inference using ee.Classifier or ee.Clusterer is generally effective up to a request size of approximately 100 megabytes. This is only an approximate guideline due to additional overhead around the request, but note that for b 100 (i.e. Since Earth Engine processes 256x256 image tiles, inference requests on imagery must have b 400 (again assuming 32-bit precision of the imagery). Examples of machine learning using the Earth Engine API can be found on the Supervised Classification page or the Unsupervised Classification page. Regression is generally performed with an ee.Reducer as described on this page, but see also ee.Reducer.RidgeRegression.
This tutorial shows you how to perform unsupervised classification (e.g., KMeans clustering) in Earth Engine. The ee.Clusterer package handles unsupervised classification (or clustering) in Earth Engine. These algorithms are currently based on the algorithms with the same name in Weka. More details about each Clusterer are available in the reference docs in the Code Editor. Clusterers are used in the same manner as classifiers in Earth Engine.
With dozens of public satellites in orbit and many more scheduled over the next decade, the size and complexity of geospatial imagery continues to grow. It has become increasingly difficult to manage this flood of data and use it to gain valuable insights. That's why we're excited to announce that we're bringing two of the most important collections of public, cost-free satellite imagery to Google Cloud: Landsat and Sentinel-2. The Landsat mission, developed under a joint program of the USGS and NASA, is the longest continuous space-based record of Earth's land in existence, dating back to 1972 with the Landsat 1 satellite. Landsat imagery sets the standard for Earth observation data due to the length of the mission and the rich data provided by its multispectral sensors.