With R GeoAI, organizations can, for example, classify satellite imagery, predict risk zones, recognize objects, or model spatial patterns using machine learning. In this blended learning course, you will learn how to develop GeoAI models in R for geographic data, remote sensing, classification, and predictive analytics. You will work with powerful packages.
R GeoAI focuses on applying machine learning and deep learning to geographic data using the R programming language. Within Geo-ICT, GeoAI is used to recognize patterns, make predictions, and automatically analyze large amounts of spatial data.
With R, geographic datasets can be combined with modern AI techniques. Examples include land-use classification, risk analysis, object recognition, remote sensing, predictive models, and the automatic discovery of spatial patterns. This creates a powerful environment for data science, GIS, and GeoAI projects.
What makes R so powerful is the combination of statistics, machine learning, programmability, and a large ecosystem of packages. This allows models to be trained, tested, and applied to geographic datasets in a reproducible manner. Within Geo-ICT, R is increasingly being used for classification, regression, pattern recognition, and smart decision support.
In this blended learning course, you will work with key GeoAI and machine learning packages such as caret, randomForest, xgboost, mlr3, tidymodels, keras, tensorflow, torch, ranger, and e1071. You will learn to build, train, evaluate, and apply models within geographic workflows.
In addition, R offers extensive capabilities for combining GeoAI with spatial analysis, remote sensing, visualization, and reporting. This makes this blended learning course particularly relevant for GIS specialists, data analysts, researchers, and Geo-ICT professionals who wish to apply AI to geographic challenges.
In this blended learning course, you’ll be introduced to the key capabilities of R for GeoAI. You’ll learn how to prepare geographic datasets for machine learning, how to train models, and how to interpret model results within a spatial context. You’ll work with packages such as caret, randomForest, xgboost, mlr3, and tidymodels.
Attention is given to classification, regression, model training, validation, and performance evaluation. You will learn how algorithms such as random forests, gradient boosting, and support vector machines can be used for Geo-ICT applications. You will also learn how to assess model results and translate them into actionable spatial insights.
In addition, you will be introduced to deep learning in R. Using packages such as Keras, TensorFlow, and Torch, you will discover how neural networks can be applied to geographic data, remote sensing, and pattern recognition. The emphasis here is on practical applications and understandable workflows.
During the blended learning program, you will work with real-world datasets and learn how to set up reproducible GeoAI workflows in R. Upon completion, you will be able to independently apply machine learning models to geographic datasets and use GeoAI results within GIS, analysis, and policy projects.
Do you already have experience with R Spatial Basics, R Data Science, or R Visualization? Then this blended learning course is a logical next step toward advanced GeoAI, predictive analytics, and smart automation within Geo-ICT.
Blended learning combines independent online learning with practical, interactive sessions, allowing you to understand both the basics of machine learning and the application of GeoAI in R. In the online modules, you’ll learn how to prepare geographic data, build models, and evaluate results using modern R packages.
You’ll discover how to develop classification and regression models for spatial problems. You’ll also learn how machine learning and deep learning can be applied to remote sensing, pattern recognition, and predictive analytics. With unlimited access to the course materials, you can review and practice the material at your own pace.
During the hands-on online sessions, you’ll apply the theory directly to realistic datasets and familiar Geo-ICT challenges. You’ll receive guidance from experienced instructors and learn how to execute GeoAI workflows using packages such as caret, randomForest, xgboost, mlr3, tidymodels, keras, tensorflow, and torch.
The combination of online learning and interactive hands-on experience ensures that you not only learn how AI models work technically, but also how to apply them responsibly within geographic analyses. After completing the blended learning program, you will be able to develop, evaluate, and practically apply GeoAI models within modern Geo-ICT projects.
You’ll receive 1-on-1 guidance. After signing up, our course coordinator will contact you to schedule your first session.
Do you have questions about the course content? Or are you unsure whether the course aligns with your learning goals or preferences? Would you prefer an in-house or private course? We’d be happy to help.
A basic knowledge of R is helpful, but extensive experience with machine learning is not required. During the blended learning course, the key GeoAI concepts are explained step by step using practical examples and geographic datasets.
During the blended learning course, you will work with classification, regression, random forests, gradient boosting, support vector machines, and deep learning techniques for geographic applications and spatial analysis.
GeoAI is used for applications such as land-use classification, remote sensing, object detection, risk analysis, forecasting, pattern recognition, mobility analysis, and smart decision-making based on geographic data.
Machine learning uses algorithms such as random forests and XGBoost to identify patterns in datasets. Deep learning uses neural networks and is primarily used for more complex analyses such as image recognition, remote sensing, and advanced pattern analysis.
During the blended learning program, you will work with tools such as caret, randomForest, xgboost, mlr3, tidymodels, Keras, TensorFlow, Torch, Ranger, and e1071 for machine learning, deep learning, and GeoAI workflows within R.
