GRASS GIS exists for more than 30 years and provides a very large and diverse set of state-of-the-art tools for the analysis of spatial data. Less known by many, remote sensing tools have been part of it almost from the beginning. GRASS GIS provides a series of imagery analysis tools for pre-processing (radiometric correction, cloud detection, pansharpening, etc), creating derived indices (vegetation indices, texture analysis, principal components, fourier transform, etc), classifying (management of training zones, different classifiers, validation tools), and producing other derived products such as evapotranspiration and energy balance models. Next to these tools for satellite images, other tools exist for the handling of aerial photography for creation of orthophotos, and for the import and analysis of Lidar data.

In addition to these tools, efforts have gone into integrating current state-of-the-art methods such as object-based image analysis (OBIA) and machine learning. A complete toolchain exists to segment images using different algorithms, to create superpixels, to collect statistics characterizing the resulting objects, and to apply machine learning algorithms for classification. New modules also include unsupervised segmentation parameter optimization and active learning. Options for pixel-based classification have also been enlarged to a host of machine learning algorithms.

In this 4-hours hands-on workshop, users will learn how to use the entire OBIA toolchain in order to create a classification of a very-high resolution image. We will go through all the steps from segmentation all the way to classification. All the steps will be run via the GRASS GIS GUI, but we will also demonstrate how these tools can be used in an automated fashion through scripts.