FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4387538148> ?p ?o ?g. }

• W4387538148 endingPage "4910" @default.
• W4387538148 startingPage "4910" @default.
• W4387538148 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." @default.
• W4387538148 created "2023-10-12" @default.
• W4387538148 creator A5002521229 @default.
• W4387538148 creator A5006988149 @default.
• W4387538148 creator A5029587041 @default.
• W4387538148 creator A5073793953 @default.
• W4387538148 date "2023-10-11" @default.
• W4387538148 modified "2023-10-12" @default.
• W4387538148 title "Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure" @default.
• W4387538148 cites W1979230542 @default.
• W4387538148 cites W1984670836 @default.
• W4387538148 cites W2047029664 @default.
• W4387538148 cites W2061577342 @default.
• W4387538148 cites W2121773078 @default.
• W4387538148 cites W2123632763 @default.
• W4387538148 cites W2152657318 @default.
• W4387538148 cites W2160544198 @default.
• W4387538148 cites W2204693006 @default.
• W4387538148 cites W2560014916 @default.
• W4387538148 cites W2799757539 @default.
• W4387538148 cites W2885995970 @default.
• W4387538148 cites W2944951724 @default.
• W4387538148 cites W2945259314 @default.
• W4387538148 cites W2963855133 @default.
• W4387538148 cites W2979283402 @default.
• W4387538148 cites W2989349076 @default.
• W4387538148 cites W3006436762 @default.
• W4387538148 cites W3014287811 @default.
• W4387538148 cites W3024005119 @default.
• W4387538148 cites W3037574091 @default.
• W4387538148 cites W3082128161 @default.
• W4387538148 cites W3088762838 @default.
• W4387538148 cites W3113540500 @default.
• W4387538148 cites W3122316811 @default.
• W4387538148 cites W3125763521 @default.
• W4387538148 cites W3164113017 @default.
• W4387538148 cites W3196530826 @default.
• W4387538148 cites W3213269499 @default.
• W4387538148 cites W4211049957 @default.
• W4387538148 cites W4221136242 @default.
• W4387538148 cites W4292064162 @default.
• W4387538148 cites W4312301518 @default.
• W4387538148 cites W4362608890 @default.
• W4387538148 cites W4386071707 @default.
• W4387538148 doi "https://doi.org/10.3390/rs15204910" @default.
• W4387538148 hasPublicationYear "2023" @default.
• W4387538148 type Work @default.
• W4387538148 citedByCount "0" @default.
• W4387538148 crossrefType "journal-article" @default.
• W4387538148 hasAuthorship W4387538148A5002521229 @default.
• W4387538148 hasAuthorship W4387538148A5006988149 @default.
• W4387538148 hasAuthorship W4387538148A5029587041 @default.
• W4387538148 hasAuthorship W4387538148A5073793953 @default.
• W4387538148 hasBestOaLocation W43875381481 @default.
• W4387538148 hasConcept C111368507 @default.
• W4387538148 hasConcept C111919701 @default.
• W4387538148 hasConcept C119857082 @default.
• W4387538148 hasConcept C121332964 @default.
• W4387538148 hasConcept C124101348 @default.
• W4387538148 hasConcept C12725497 @default.
• W4387538148 hasConcept C127313418 @default.
• W4387538148 hasConcept C127413603 @default.
• W4387538148 hasConcept C1276947 @default.
• W4387538148 hasConcept C136428324 @default.
• W4387538148 hasConcept C146978453 @default.
• W4387538148 hasConcept C153294291 @default.
• W4387538148 hasConcept C154945302 @default.
• W4387538148 hasConcept C166689943 @default.
• W4387538148 hasConcept C169258074 @default.
• W4387538148 hasConcept C19269812 @default.
• W4387538148 hasConcept C204366326 @default.
• W4387538148 hasConcept C205649164 @default.
• W4387538148 hasConcept C22286887 @default.
• W4387538148 hasConcept C39399123 @default.
• W4387538148 hasConcept C41008148 @default.
• W4387538148 hasConcept C554190296 @default.
• W4387538148 hasConcept C62649853 @default.
• W4387538148 hasConcept C71813955 @default.
• W4387538148 hasConcept C76155785 @default.
• W4387538148 hasConcept C81692654 @default.
• W4387538148 hasConcept C87360688 @default.
• W4387538148 hasConcept C93518851 @default.
• W4387538148 hasConceptScore W4387538148C111368507 @default.
• W4387538148 hasConceptScore W4387538148C111919701 @default.
• W4387538148 hasConceptScore W4387538148C119857082 @default.
• W4387538148 hasConceptScore W4387538148C121332964 @default.
• W4387538148 hasConceptScore W4387538148C124101348 @default.
• W4387538148 hasConceptScore W4387538148C12725497 @default.
• W4387538148 hasConceptScore W4387538148C127313418 @default.
• W4387538148 hasConceptScore W4387538148C127413603 @default.
• W4387538148 hasConceptScore W4387538148C1276947 @default.
• W4387538148 hasConceptScore W4387538148C136428324 @default.
• W4387538148 hasConceptScore W4387538148C146978453 @default.
• W4387538148 hasConceptScore W4387538148C153294291 @default.
• W4387538148 hasConceptScore W4387538148C154945302 @default.
• W4387538148 hasConceptScore W4387538148C166689943 @default.
• W4387538148 hasConceptScore W4387538148C169258074 @default.
• W4387538148 hasConceptScore W4387538148C19269812 @default.

• next