GEE for Spatial Data

Google Earth Engine for spatial data

Google Earth Engine (GEE) is a powerful cloud-based platform for analyzing and visualizing geospatial data. It gives you access to a massive library of satellite imagery and spatial datasets, making it easy to track landscape changes and carry out complex analyses—without needing high-end hardware. With built-in cloud computing, you can process large datasets and perform advanced calculations, from time-series analysis to machine learning classification.

GEE is used globally in fields like environmental research, urban planning, and agriculture. Scientists rely on it to map deforestation, analyze ecosystems, and monitor climate change. Urban planners use it for tracking development, monitoring air quality, and planning sustainable cities. In agriculture, it’s used to monitor crop health, assess soil conditions, and predict the effects of weather changes.

On top of its powerful tools, GEE offers a collaborative environment where users can share scripts and visualizations. This makes it a valuable platform for researchers, governments, and businesses that work with geospatial data. In this course, you’ll learn how to use GEE to transform complex spatial data into practical insights you can use in the real world.

What will you learn in this blended learning course?

This course dives into the full potential of Google Earth Engine for spatial data analysis. You’ll learn how to process satellite imagery, identify spatial patterns, and analyze large datasets with the power of cloud computing. It’s hands-on, practical, and built for real-world applications.

You’ll start by working with raw satellite data and learn how to visualize and interpret spatial information. For example, you’ll detect changes in land use—like deforestation or water level shifts—and use machine learning tools in GEE to predict patterns and address complex challenges.

Alongside core concepts, you’ll complete assignments using real datasets tied to current topics in environmental science, city planning, and agriculture. You’ll also experiment with GEE scripting and automation tools to efficiently manage large volumes of spatial data. Through practical exercises, you’ll learn to turn your analysis into usable insights for your field.

Why choose this course Google Earth Engine for Spatial data?

Blended learning gives you the best of both worlds—live interaction and flexible self-paced study—so you can build real, job-ready skills in geospatial analysis with GEE. In this course, you’ll learn to process satellite imagery, apply machine learning, and turn spatial data into clear, useful insights.

We begin with a live session where you’ll jump right into working with real geospatial datasets. With support from expert instructors, you’ll learn how to process imagery, visualize changes, and run targeted analyses using GEE.

Next, you’ll move into self-paced modules that let you explore topics like remote sensing, pattern recognition, and workflow automation. You’ll work with tools such as NDVI and Random Forest to detect land use changes, vegetation patterns, and more.

Then, in a second live session, you’ll apply what you’ve learned to real case studies. You’ll refine your approach, solve realistic analysis problems, and get direct feedback to sharpen your skills.

One of the course highlights is its case-based approach. You’ll create practical outputs—like change detection maps and automated classification scripts—that you can use right away in your own work.

By combining expert-led sessions with flexible online learning, this course helps you move beyond theory. You’ll finish with the confidence and skills to process, analyze, and automate spatial data in GEE—and apply it to real-world challenges in your field.