Take your geospatial skills to the next level in this hands-on, two-day Google Earth Engine (GEE) advanced course. Built for professionals and researchers with prior GEE experience, this course dives deep into satellite imagery analysis, vegetation indices, and machine learning techniques. Through real-world examples and guided exercises, you’ll gain the confidence and know-how to solve complex environmental challenges using one of the most powerful platforms in geospatial science.
In today’s data-driven world, geographic information systems (GIS) and platforms like Google Earth Engine are transforming how we understand, manage, and protect our planet. Whether it’s monitoring deforestation, planning urban growth, or tracking agricultural health, analyzing spatial data is essential for making smarter, faster decisions.
At the center of this shift is Google Earth Engine (GEE). It’s a cloud-based platform that gives you access to petabytes of satellite imagery and geospatial datasets — without requiring high-performance hardware. With its browser-based code editor and powerful processing tools, GEE brings large-scale environmental analysis within reach.
What makes this platform so impactful is its ability to link data with specific locations. As a result, it transforms raw numbers into interactive maps and layered visualizations. GIS is already being used across public health, conservation, disaster response, and agriculture. Furthermore, when combined with machine learning, GEE allows for advanced tasks such as land use classification, ecosystem forecasting, and climate impact analysis.
Given these trends, demand is growing for professionals who can work with geospatial data. This course is your opportunity to build those skills and apply them with confidence.
This course builds on your foundational knowledge of GEE and explores advanced techniques in vegetation analysis and machine learning. Through guided exercises and real-world datasets, you’ll gain both theoretical insight and hands-on experience.
To begin with, you’ll work with vegetation indices like NDVI, NDRE, EVI, and MCARI. You’ll apply them to real use cases in agriculture, forestry, and urban planning. In addition, you’ll write and optimize JavaScript code to efficiently process large-scale satellite data.
As the course progresses, you’ll explore supervised and unsupervised machine learning techniques. You’ll use these to classify land cover, detect changes over time, and uncover environmental trends. Moreover, you’ll interpret results using zonal statistics, maps, and graphs. You’ll also evaluate model accuracy using tools such as confusion matrices — ensuring your analysis is both valid and reliable.
We don’t just teach software—we help you build practical, future-ready skills that you can apply right away. This course is built for learners who want to go beyond the basics and explore the full potential of Google Earth Engine.
Here’s what makes our course stand out:
On the first day, you’ll dive into vegetation indices and their role in environmental monitoring. You’ll begin by exploring the core concepts behind spectral indices and their practical applications in different fields. Using Sentinel-2 and MODIS satellite datasets, you’ll learn how to calculate common indices like NDVI and EVI, apply them to real-world scenarios, and detect changes over time. You’ll also practice working with time series data and learn to filter and process imagery effectively. To wrap up the day, you’ll create clear visualizations and statistical graphs that help translate raw data into meaningful insights.
On the second day, the focus shifts to applying machine learning in a geospatial context. You’ll start by understanding the basics of machine learning and how it integrates with satellite image analysis. Then you’ll build classification models using both supervised and unsupervised methods in GEE. You’ll label training datasets, train your models, and evaluate performance using accuracy metrics like confusion matrices. Throughout the day, real-life case studies will guide your learning, helping you apply these techniques to tasks such as land use classification and environmental change detection.
Do you have questions about the course content? Not sure if the course aligns with your learning objectives? Or would you prefer a private session or in-company training? We’re happy to assist—feel free to get in touch.
We aim to make our courses accessible to as many people as possible. If the course fee is a concern, instead of registering directly, you can indicate on the registration form that you’d like to receive a quote tailored to your needs. In many cases, we can offer flexible solutions. For instance, we can adjust the course content for large groups, shorten the course based on existing knowledge, or offer daily rates to suit your requirements.
Yes, you can reach out to the instructor with questions for up to 2 weeks after the course. Since the instructor is likely teaching other courses, we recommend emailing your questions to info@geo-ict.com. We’ll forward your inquiry to the instructor, and you’ll receive a response within 24 hours. After the 2-week period, we recommend using our Personalized Online Support for continued assistance.
Yes, we offer on-site training regularly throughout the Netherlands. Our instructor will bring laptops for the participants, and all you need to do is arrange a suitable room at your location.
Please send your requirements to info@geo-ict.com, and we will provide a customized quote, which will include travel and accommodation costs. Once the quote is confirmed, our course coordinator will reach out to schedule the training days.
After each course, participants receive a link to our evaluation portal where you can share your feedback on what you liked and didn’t like. We strive to provide a great experience for all our participants, but if you have a complaint, please click on ‘Complaints Procedure’ in the portal. This document will guide you through the steps to take. Geo-ICT Training Center, Netherlands, is a member of the Dutch Council for Training and Education (NRTO), ensuring a fair and transparent process.
Courses are typically scheduled according to the Dutch time zone, with sessions running from 9:00 AM to 12:00 PM and 1:00 PM to 4:00 PM. For participants in different time zones, we adjust the course times in consultation with you.
