  A combinatory approach of two well-known fields: deep learning and semi
supervised learning is presented, to tackle the land cover identification
problem. The proposed methodology demonstrates the impact on the performance of
deep learning models, when SSL approaches are used as performance functions
during training. Obtained results, at pixel level segmentation tasks over
orthoimages, suggest that SSL enhanced loss functions can be beneficial in
models' performance.
