Self-paced Tutorials
These tutorials are an introduction to using the Earth Engine JavaScript API for advanced geospatial analysis. The tutorials assume no programming background, although they do assume a willingness to learn some JavaScript programming. Use the links below to get started on the tutorials or use the menus on the left to jump to a section of interest.
- Introduction to JavaScript for Earth Engine
- Introduction to the Earth Engine JavaScript API
- Introduction to Global Forest Change datasets
- Introduction to the JRC Global Surface Water dataset
Video Tutorials
These video tutorials are from lectures or hands-on trainings conducted at the Earth Engine Users' Summit. View the videos after completeing the self-paced tutorials.
- Introduction to Earth Engine (condensed)
- Hands-on Intermediate Training
- Tables and Vectors
- Importing and Exporting
- Classification
- Machine Learning
- Arrays and Matrices
- Time Series Analysis
- Earth Engine and the Google Cloud Platform
- Google Maps API
- Publishing and Storytelling
- Datasets
Introduction to Earth Engine (condensed)
Learn about Earth Engine from developer advocate Noel Gorelick in this fast-paced intro.
companion slidescode repository
Hands-on Intermediate Training
Topics covered include classification, spectral unmixing and terrain visualization.
Tables and Vectors
Overview of tables and vectors in Earth Engine. Topics include how to load, manipulate, display and analyze vector and tabular data in Earth Engine.
companion slidesImporting and Exporting
Topics covered include importing and exporting Earth Engine data, the command line interface, and map publishing.
Classification
Learn all about classifiers (both supervised and unsupervised), training data, test data, the dreaded "computed value too large" message, and the awesome linear regression reducer.
companion slidescode repository
Topics covered include supervised and unsupervised classification.
Machine Learning
Machine Learning Best Practices
With the pace of modern machine learning, building and training neural networks is hard. Learn some best practices and sift through the overwhelming amount of information available with a focus on remote sensing.
companion slidesNeural Segmentation
Arguably the most common application of ML to earth observation imagery is pixel level segmentation and regression. With neural networks, a whole new set of techniques are possible that eclipse existing methods in terms of generality, but come with their own set of challenges. Learn applications and training regimens ranging from supervised to fully unsupervised.
companion slidesArrays and Matrices
Overview of array and matrix operations the Earth Engine way. Topics include linear modeling, matrix solving, eigen analysis, covariance reducers.
companion slidesTime Series Analysis
Overview of time series analysis the Earth Engine way. Topics covered include linear modeling, auto-correlation, cross-correlation, auto-regressive models and smoothing.
companion slidesEarth Engine and the Google Cloud Platform
Introduction to interoperability between Earth Engine and the Google Cloud Platform.
companion slidesGoogle Maps API
Create a "Hello World" Maps API webpage, and learn how to do things like change the options (background map type, initial location, etc.), overlay data and KML layers, and start to showcase your first Earth Engine maps.
companion slidesPublishing and storytelling with your Earth Engine results
Get an overview of how to use Google's geo tools to tell your stories and share your data. Topics include Google Earth, Google Maps (APIs), My Maps, Tour Builder, and Street View.
companion slidesDatasets
Synthetic Aperture Radar (Sentinel-1)
Take a deep dive into one of the more unique datasets in the Earth Engine data catalog. This session provides an introduction to Synthetic Aperture Radar (SAR) data and working with scripts analyzing Sentinel-1 SAR data.
companion slides