  With the rapid development of Remote Sensing acquisition techniques, there is
a need to scale and improve processing tools to cope with the observed increase
of both data volume and richness. Among popular techniques in remote sensing,
Deep Learning gains increasing interest but depends on the quality of the
training data. Therefore, this paper presents recent Deep Learning approaches
for fine or coarse land cover semantic segmentation estimation. Various 2D
architectures are tested and a new 3D model is introduced in order to jointly
process the spatial and spectral dimensions of the data. Such a set of networks
enables the comparison of the different spectral fusion schemes. Besides, we
also assess the use of a " noisy ground truth " (i.e. outdated and low spatial
resolution labels) for training and testing the networks.
