Total study time: 24 hours
How can you combine machine learning with spatial data in R? In this one-on-one online course, you’ll learn to analyze geospatial datasets and build models using packages like sf, raster, and caret. You’ll apply classification, clustering, and regression to real data—and discover how these techniques support GIS, urban planning, and environmental analysis. With flexible online modules and virtual hands-on sessions, you’ll quickly build practical, job-ready skills.
Machine learning enables computers to detect patterns and make predictions from data. When applied to geospatial data—such as satellite imagery or digital maps—it unlocks powerful tools for spatial analysis.
R is a widely used language for data science. It’s especially effective for working with large volumes of spatial data and building predictive models. For example, you can use R to detect land-use change, optimize traffic systems, or assess flood risk.
A great use case is automated image recognition. It allows you to analyze satellite photos and monitor urban growth or deforestation over time. You can also use clustering and classification to group areas based on characteristics like population density or infrastructure.
By combining geodata with machine learning, you can create smarter models that support better spatial decisions. In this course, you’ll learn how to clean, process, and analyze spatial data in R, apply ML algorithms, and visualize your results effectively.
This course shows you how to apply machine learning directly to geospatial data to tackle complex spatial problems. You’ll dive into real datasets, clean and prepare them, and get them ready for modeling.
You’ll start by detecting spatial patterns and building predictive or classification models using powerful algorithms like decision trees, random forests, and neural networks.
Then, you’ll use clustering and classification techniques to group areas by land use, population density, or other features. You’ll also evaluate your models, fine-tune their performance, and improve their accuracy.
By the end, you’ll confidently use R to solve real-world geospatial challenges—whether in urban planning, environmental research, or GIS projects.
Blended learning gives you the best of both worlds—live support and flexible, self-paced study—so you can develop real, applicable skills in machine learning with spatial data using R.
We kick off with a live session where you’ll dive into actual geospatial datasets. With guidance from experienced GIS and R instructors, you’ll explore key ML concepts, build your first models, and visualize insights through maps and charts.
Then, in our self-paced modules, you’ll deepen your skills at your own pace. You’ll work through topics like data cleaning, feature engineering, and predictive modeling. Along the way, you’ll apply clustering, classification, and regression techniques to real-world datasets using libraries like caret, sf, and raster.
Later, in a second live session, you’ll apply everything you’ve learned to realistic challenges. You’ll refine your workflow, troubleshoot issues, and get tailored feedback to strengthen your approach.
A key feature of this course is its case-based structure. You’ll build real outputs—like prediction maps and model reports—that can be used immediately in your work.
By combining expert-led sessions with flexible learning, this course takes you beyond the basics. By the end, you’ll know how to analyze geospatial data, create reliable ML models in R, and turn your findings into smart, data-driven decisions.
After completing this course, you will be able to:
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.