Solar granulation is the visible signature of convective cells at the solar surface. The granulation cellular pattern observed in the continuum intensity images is characterised by diverse structures e.g., bright individual granules of hot rising gas or dark intergranular lanes. Recently, the access to new instrumentation capabilities has given us the possibility to obtain high-resolution images, which have revealed the overwhelming complexity of granulation (e.g., exploding granules and granular lanes). In that sense, any research focused on understanding solar small-scale phenomena on the solar surface is sustained on the effective identification and localization of the different resolved structures. In this work, we present the initial results of a proposed classification model of solar granulation structures based on neural semantic segmentation. We inspect the ability of the U-net architecture, a convolutional neural network initially proposed for biomedical image segmentation, to be applied to the dense segmentation of solar granulation. We use continuum intensity maps of the IMaX instrument onboard the Sunrise I balloon-borne solar observatory and their corresponding segmented maps as a training set. The training data have been labeled using the multiple-level technique (MLT) and also by hand. We performed several tests of the performance and precision of this approach in order to evaluate the versatility of the U-net architecture. We found an appealing potential of the U-net architecture to identify cellular patterns in solar granulation images reaching an average accuracy above 80% in the initial training experiments.