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

Sparse InSAR Data 3D Inpainting for Ground Deformation Detection Along the Rail Corridor