Satellite interferometric data for seismic damage assessment

Giordano, Pier Francesco; Miraglia, Gaetano; Lenticchia, Erica; Ceravolo, Rosario; Limongelli, Maria Pina

doi:10.1016/j.prostr.2023.01.201

Cited by 1 publication

(1 citation statement)

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“…2) Heave: Swelling or rising of the ground, causing the foundation of a building to lift and shift upward [2]. 3) Lateral movement: Horizontal movement of a building caused by earthquakes, landslides, or soil erosion [3]. 4) Slope instability: Instability of the ground beneath a building, causing the structure to tilt or lean [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection of Building Displacements Through Unsupervised Learning From InSAR Data

Kuzu,

Bagaglini,

Wang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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-series that are not aligned in time, unlike the other conventional reconstruction losses that do not account for time shifts. Moreover, we enhance the performance of anomaly detection by smoothing inputs with a Hann window and defining the learning objective to reconstruct the time order of randomly permuted input series. Our experimental findings, based on persistent scatterer data from Rome, Italy, reveal that our method outperforms several unsupervised machine learning and deep learning methods in detecting various types of building displacement, such as trend, noise, and step anomalies. Additionally, quantitative and qualitative evaluations validate the efficacy of our approach in identifying potentially anomalous buildings. Thus, our method offers a promising solution for detecting anomalies in PS-InSAR time-series, which could have substantial implications in the fields of urban monitoring and infrastructure management.

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Section: Introductionmentioning

confidence: 99%