# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 5 Conclusions  
A novel methodology based on Entity Embeddings and Deep Learning (EE-DL) was proposed for detecting anomalous InSAR deformation. Embeddings of InSAR metadata were used for training a fully connected neural network. The use of entity embeddings captured both the spatial and temporal components of the InSAR measurements, without the typical DL challenges related to InSAR data. The feasibility of the EE-DL approach was demonstrated by a failed TSF case study to detect anomalous deformation behaviour preceding failure. The performance of the EE-DL model was compared to a probabilistic approach, GPR, and a simple baseline model, RF.  
The comparison outlined that the EE-DL approach performed better than both GPR and RF for predicting ground deformation from interferometric time series. The novel use of entity embeddings provided an accurate geolocation of anomalous areas that could be subject to failure. The most true anomalous clusters were detected using the GPR uncertainty envelope. However, these anomalies were at the expense of the false positives,  
Figure 12: Summary count of the number of anomalous clusters detected by (**a**) EE-DL, (**b**) GPR mean, and (**c**) RF.  
limiting its ability to geolocate the failure area. The EE-DL and RF approaches did not have an associated probabilistic uncertainty envelope; therefore, the background noise level of the predictions, an RMSE of \(\pm\)4 mm, was used for defining an anomaly threshold. When this definition of anomaly was applied to the GPR mean predictions, GPR performance improved compared to the performance of its uncertainty envelope. The EE-DL approach still performed better than the GPR, because the improved GPR performance was at the expense of a significantly lowered detection of total anomalies, both true and false positives. The EE-DL also performed better than the RF, as the RF consistently detected more false positives than true positives.  
The conclusions from this study set a promising direction for the establishment of early-warning systems for infrastructure failure with remote-sensing-derived data. These systems, alongside ground-based instrumentation, geotechnical simulation, and inspection, can create a monitoring ecosystem aimed at mitigating and, ideally, preventing the catastrophic consequences of TSF failures.  
Conceptualisation, M.B., C.R. and B.S.; methodology, M.B.; software, M.B.; validation, M.B.; formal analysis, M.B.; investigation, M.B.; resources, M.B.; data curation, M.B.; writing--original draft preparation, M.B.; writing--review and editing, supervision, C.R., F.K. and B.S.; project administration, M.B.; funding acquisition, M.B., C.R. and B.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Royal Commission for the Exhibition of 1851 and the Royal Academy of Engineering under the Industrial Fellowship and the Research Fellowship schemes, respectively.  
Third-party data restrictions apply to the availability of these data. The data were obtained from Terra Motion Limited and are available from the authors with the permission of Terra Motion Limited.  
The authors wish to thank the financial support of the Royal Commission for the Exhibition of 1851 and the Royal Academy of Engineering. The authors wish to thank Terra Motion Limited for providing with ISBAS data over the test site.  
The authors declare no conflict of interest.