  Multi-image super-resolution from multi-temporal satellite acquisitions of a
scene has recently enjoyed great success thanks to new deep learning models. In
this paper, we go beyond classic image reconstruction at a higher resolution by
studying a super-resolved inference problem, namely semantic segmentation at a
spatial resolution higher than the one of sensing platform. We expand upon
recently proposed models exploiting temporal permutation invariance with a
multi-resolution fusion module able to infer the rich semantic information
needed by the segmentation task. The model presented in this paper has recently
won the AI4EO challenge on Enhanced Sentinel 2 Agriculture.
