  High resolution remote sensing imagery is used in broad range of tasks,
including detection and classification of objects. High-resolution imagery is
however expensive, while lower resolution imagery is often freely available and
can be used by the public for range of social good applications. To that end,
we curate a multi-spectral multi-image super-resolution dataset, using
PlanetScope imagery from the SpaceNet 7 challenge as the high resolution
reference and multiple Sentinel-2 revisits of the same imagery as the
low-resolution imagery. We present the first results of applying multi-image
super-resolution (MISR) to multi-spectral remote sensing imagery. We,
additionally, introduce a radiometric consistency module into MISR model the to
preserve the high radiometric resolution of the Sentinel-2 sensor. We show that
MISR is superior to single-image super-resolution and other baselines on a
range of image fidelity metrics. Furthermore, we conduct the first assessment
of the utility of multi-image super-resolution on building delineation, showing
that utilising multiple images results in better performance in these
downstream tasks.
