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The use of entity embeddings captures both the spatial and temporal components of the InSAR measurements, without requiring the generation of image data. This makes the model more flexible and overcomes typical DL challenges around the handling of the inherent spatial data gaps present in InSAR.
Oct 11, 2023
This study demonstrates that EE-DL can detect and predict the fine spatial movement patterns that eventually resulted in the failure. We also compare the ...
ABSTRACT. A novel methodology for detecting anomalous deformation behaviour from satellite-Synthetic Aperture Radar Interfer- ometry (InSAR) is proposed.
Jul 14, 2023 · ENTITY EMBEDDINGS IN DEEP LEARNING FOR THE DETECTION OF ANOMALOUS INSAR DEFORMATION SIGNALS. Maral Bayaraa, University of Oxford, United ...
Request PDF | On Jul 16, 2023, M. Bayaraa and others published Entity Embeddings in Deep Learning for the Detection of Anomalous Insar Deformation Signals ...
Oct 9, 2023 · 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 ...
Entity Embeddings in Deep Learning for the Detection of Anomalous Insar Deformation Signals. M Bayaraa, C Rossi, A Kalaitzis, B Sheil. IGARSS 2023-2023 IEEE ...
Entity Embeddings in Deep Learning for the Detection of Anomalous Insar Deformation Signals. Maral Bayaraa, Cristian Rossi, Alfredo Kalaitzis, Brian Sheil.
Entity Embeddings in Remote Sensing: Application to Deformation ... Entity Embeddings in Deep Learning for the Detection of Anomalous Insar Deformation Signals.
... Entity Embeddings within a Deep Learning framework (EE-DL) is proposed for detecting anomalous InSAR deformation. Embeddings are similar to the “word ...