Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
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Published:2023-10-11
Issue:20
Volume:15
Page:4910
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ISSN:2072-4292
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Container-title:Remote Sensing
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language:en
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Short-container-title:Remote Sensing
Author:
Bayaraa Maral12ORCID, Rossi Cristian23ORCID, Kalaitzis Freddie4ORCID, Sheil Brian5
Affiliation:
1. Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, UK 2. Satellite Applications Catapult, Didcot OX11 0QR, UK 3. School of Geography and the Environment, University of Oxford, Oxford OX1 3QY, UK 4. Department of Computer Science, University of Oxford, Oxford OX1 3QG, UK 5. Department of Engineering, University of Cambridge, Cambridge CB3 0FA, UK
Abstract
There is a critical need for a global monitoring capability for Tailings Storage Facilities (TSFs), to help protect the surrounding communities and the environment. Satellite Synthetic Aperture Radar Interferometry (InSAR) shows much promise towards this ambition. However, extracting meaningful information and interpreting the deformation patterns from InSAR data can be a challenging task. One approach to address this challenge is through the use of data science techniques. In this study, the representation of InSAR metadata as Entity Embeddings within a Deep Learning framework (EE-DL) is investigated for modelling the spatio-temporal deformation response. Entity embeddings are commonly used in natural-language-processing tasks. They represent discrete objects, such as words, as continuous, low-dimensional vectors that can be manipulated mathematically. We demonstrate that EE-DL can be used to predict anomalous patterns in the InSAR time series. To evaluate the performance of the EE-DL approach in SAR interferometry, we conducted experiments over a mining test site (Cadia, Australia), which has been subject to a TSF failure. This study demonstrated that EE-DL can detect and predict the fine spatial movement patterns that eventually resulted in the failure. We also compared the results with deformation predictions from common baseline models, the Random Forest model and Gaussian Process Regression (GPR). Both EE-DL and GPR greatly outperform Random Forest. While GPR is also able to predict displacement patterns with millimetric accuracy, it detects a significantly lower number of anomalies compared to EE-DL. Overall, our study showed that EE-DL is a promising approach for building early-warning systems for critical infrastructures that use InSAR to predict ground deformations.
Funder
Royal Commission for the Exhibition of 1851 Royal Academy of Engineering
Subject
General Earth and Planetary Sciences
Reference53 articles.
1. Hund, K., La Porta, D., Fabregas, T., Laing, T., and Dexhage, J. (2020). Minerals for Climate Action: The Mineral Intensity of the Clean Energy Transition. Climate-Smart Mining Facility, World Bank. 2. Rossi, C., Bateson, L., Bayaraa, M., Butcher, A., Ford, J., and Hughes, A. (2022). Framework for remote sensing and modelling of lithium-brine deposit formation. Remote Sens., 14. 3. (2023, July 13). WMTF. Available online: https://worldminetailingsfailures.org/estimate-of-world-tailings-portfolio-2020/. 4. Robertson, P., de Melo, L., Williams, D.J., and Wilson, G.W. (2023, July 13). Report of the Expert Panel on the Technical Causes of the Failure of Feijão Dam I. Available online: http://www.b1technicalinvestigation.com/. 5. Public remotely sensed data raise concerns about history of failed Jagersfontein dam;Sci. Rep.,2023
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