Automatic Detection of Building Displacements Through Unsupervised Learning From InSAR Data
Rdvan Salih Kuzu,
Leonardo Bagaglini,
Yi Wang
et al.
Abstract:We introduce an unsupervised learning method that aims to identify building anomalies using Interferometric Synthetic Aperture Radar (InSAR) time-series data. Specifically, we leverage data obtained from the European Ground Motion Service to develop our proposed approach, which employs a long short-term memory autoencoder model and a reconstruction loss function based on a soft variant of the dynamic time warping, namely "soft-DTW". We deliberately utilize this loss function for its ability to compare time-ser… Show more
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