  The Sentinel-2 satellite mission delivers multi-spectral imagery with 13
spectral bands, acquired at three different spatial resolutions. The aim of
this research is to super-resolve the lower-resolution (20 m and 60 m Ground
Sampling Distance - GSD) bands to 10 m GSD, so as to obtain a complete data
cube at the maximal sensor resolution. We employ a state-of-the-art
convolutional neural network (CNN) to perform end-to-end upsampling, which is
trained with data at lower resolution, i.e., from 40->20 m, respectively
360->60 m GSD. In this way, one has access to a virtually infinite amount of
training data, by downsampling real Sentinel-2 images. We use data sampled
globally over a wide range of geographical locations, to obtain a network that
generalises across different climate zones and land-cover types, and can
super-resolve arbitrary Sentinel-2 images without the need of retraining. In
quantitative evaluations (at lower scale, where ground truth is available), our
network, which we call DSen2, outperforms the best competing approach by almost
50% in RMSE, while better preserving the spectral characteristics. It also
delivers visually convincing results at the full 10 m GSD. The code is
available at https://github.com/lanha/DSen2
