  Monitoring of ground movement close to the rail corridor, such as that
associated with landslips caused by ground subsidence and/or uplift, is of
great interest for the detection and prevention of possible railway faults.
Interferometric synthetic-aperture radar (InSAR) data can be used to measure
ground deformation, but its use poses distinct challenges, as the data is
highly sparse and can be particularly noisy. Here we present a scheme for
processing and interpolating noisy, sparse InSAR data into a dense
spatio-temporal stack, helping suppress noise and opening up the possibility
for treatment with deep learning and other image processing methods.
