With R Drones, organizations can, for example, analyze drone imagery for vegetation, infrastructure, construction sites, levees, object recognition, or LiDAR point clouds. In this blended learning course, you will learn how to process and analyze drone imagery, aerial photographs, and point clouds. The blended learning course combines drones, remote sensing, image analysis, and Geo-ICT into practical workflows for spatial inspections and analyses.
R Drones focuses on processing, analyzing, and interpreting drone imagery, aerial photographs, and LiDAR point clouds using the R programming language. Within Geo-ICT, this is important for applications such as inspections, terrain analysis, vegetation monitoring, infrastructure, construction projects, and spatial quality control.
With R, drone data can be converted into actionable insights. This includes analyzing point clouds, recognizing objects in aerial photos, performing image processing, and combining drone imagery with other geographic datasets. This creates a powerful environment for remote sensing, image analysis, and Geo-ICT workflows.
What makes R so powerful is the combination of programmability, statistics, and specialized packages for LiDAR and image processing. This allows analyses to be performed in a reproducible manner and easily repeated for multiple projects or measurement campaigns. Within Geo-ICT, R is increasingly being used for the automated processing of drone and aerial image data.
In this blended learning course, you will work with key packages such as lidR, OpenImageR, imager, and EBImage. You will learn to analyze LiDAR point clouds, process aerial photos, recognize image features, and perform basic steps for classification and object detection.
In addition, R offers extensive capabilities for combining drone data with GIS data, visualization, statistics, and reporting. This makes this blended learning course particularly relevant for GIS specialists, drone pilots, surveyors, inspectors, ecologists, and Geo-ICT professionals who wish to apply drone imagery and point clouds in practical ways for spatial analysis.
In this blended learning course, you will be introduced to the key capabilities of R for drone and image analysis. You will learn how to process and analyze drone imagery, aerial photos, and point clouds within reproducible workflows. You will work with packages such as lidR, OpenImageR, imager, and EBImage.
The course covers LiDAR point clouds, elevation data, image processing, object recognition, and classification. You’ll learn how to prepare, analyze, and combine drone data with other spatial data sources within Geo-ICT projects.
You will also learn how R can be used for inspections and monitoring. Think of applications for vegetation, infrastructure, construction sites, levees, objects, and terrain models. You will also discover how image analysis can help automatically recognize patterns, anomalies, and spatial features.
During the blended learning program, you’ll work with practical datasets and learn how to set up reproducible drone workflows in R. Upon completion, you’ll be able to independently process and analyze drone imagery, aerial photos, and point clouds for GIS, inspection, and remote sensing projects.
Do you already have experience with R Spatial Basics, R Visualization, or R GeoAI? Then this blended learning course is a logical next step toward drone image analysis, LiDAR processing, and automated image interpretation within Geo-ICT.
Blended learning combines independent online learning with hands-on, interactive sessions, allowing you to understand both the technical fundamentals and the practical application of drone analysis in R. In the online modules, you’ll learn how to process drone imagery, aerial photos, and LiDAR point clouds using modern R packages.
You’ll discover how to work with point clouds, image data, classification, and object recognition. You’ll also learn how to combine drone data with GIS data, visualizations, and reports within R. 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 immediately apply the theory to realistic datasets and familiar Geo-ICT challenges. You’ll receive guidance from experienced instructors and learn how to execute drone workflows using packages such as lidR, OpenImageR, imager, and EBImage.
The combination of online learning and interactive hands-on experience ensures that you not only learn how drone and image data are technically processed, but also how to translate this data into actionable spatial insights. After completing the blended learning program, you will be able to professionally analyze drone imagery and point clouds within Geo-ICT, remote sensing, and inspection 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 understanding of GIS and R is helpful, but experience with drones or remote sensing is not required. During the blended learning course, drone imagery, point clouds, and image analysis are explained step by step using practical Geo-ICT examples.
During the blended learning program, you will work with drone imagery, aerial photographs, orthophotos, raster data, and LiDAR point clouds for spatial analysis, inspection, and monitoring within Geo-ICT workflows.
R is used for vegetation monitoring, infrastructure inspections, terrain analysis, object detection, classification, elevation analysis, construction projects, ecological monitoring, and LiDAR data processing.
Yes. Drone imagery, point clouds, and analysis results can be easily combined with GIS software such as QGIS and, to a lesser extent, ArcGIS for further analysis, visualization, and map production within Geo-ICT projects.
During the blended learning program, you will work with tools such as lidR, OpenImageR, imager, and EBImage for LiDAR analysis, image processing, classification, object recognition, and drone image analysis within R.
