The Concrete Aggregate Dataset consists of high resolution images acquired from 40 different concrete cylinders, cut lengthwise as to display the particle distribution in the concrete, with a ground sampling distance of 0.03mm. In order to train and evaluate approaches for the semantic segmentation of the concrete aggregate images, currently 17 of the 40 images have been annotated by manually associating one of the classes aggregate or suspension to each pixel. We encourage to use the remaining unlabelled images for semi-supervised segmentation approaches, in which unlabelled data is leveraged in addition to labelled training data in order to improve the segmentation performance.