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The large volumes of Sentinel-1 data produced over Europe are being used todevelop pan-national ground motion services. However, simple analysistechniques like thresholding cannot detect and classify complex deformationsignals reliably making providing usable information to a broad range ofnon-expert stakeholders a challenge. Here we explore the applicability of deeplearning approaches by adapting a pre-trained convolutional neural network(CNN) to detect deformation in a national-scale velocity field. For ourproof-of-concept, we focus on the UK where previously identified deformation isassociated with coal-mining, ground water withdrawal, landslides andtunnelling. The sparsity of measurement points and the presence of spike noisemake this a challenging application for deep learning networks, which involvecalculations of the spatial convolution between images. Moreover, insufficientground truth data exists to construct a balanced training data set, and thedeformation signals are slower and more localised than in previousapplications. We propose three enhancement methods to tackle these problems: i)spatial interpolation with modified matrix completion, ii) a synthetic trainingdataset based on the characteristics of real UK velocity map, and iii) enhancedover-wrapping techniques. Using velocity maps spanning 2015-2019, our frameworkdetects several areas of coal mining subsidence, uplift due to dewatering,slate quarries, landslides and tunnel engineering works. The resultsdemonstrate the potential applicability of the proposed framework to thedevelopment of automated ground motion analysis systems.

Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data