# High Performance Computing in Satellite SAR Interferometry: A Critical Perspective
## 6 Selected MT-InSAR Techniques Using HPC or Cloud-Based Platforms
### MT-InSAR Processing via Cloud-Based Platforms and Services  
Recently, a general trend of public and private institutions is to offer end-user services based on EO data to geohazard scientists who do not own the necessary computing power and storage capacity. Indeed, this trend was essentially driven by the strategic funding of cloud-based platforms deployment by the European Commission to provide centralized access to Copernicus data. To overcome a significant limitation inherent in cloud-based platforms, a possible approach is to locate computational resources close to the SAR data archives. Remarkably, the European Space Agency (ESA) has funded the Geohazards Exploitation Platform (GEP), which is a web-based platform launched in 2015 and designed to exploit EO data for assessing geobazards [217]. The ESA platform is equipped with a large set of EO processing services. Accordingly, the platform offers direct access to the freely available satellite SAR data archives and the computing power at the same time while providing a rich set of EO data processing tools. In the following, we focus on two different InSAR processing services offered at the server-side by the ESA's computing facilities. First, the P-SBAS algorithm [204] has been implemented as a web tool of the ESA's Grid Processing On-Demand (G-POD) environment [217]. However, the implementation of the on-demand service in GEP has some practical limitations, insofar as some processing parameters are out of the control of the users, as pointed out in [217; 218]. In addition, the use of the term \"unsupervised\" in this context appears rather inappropriate [218]. In this regard, it is helpful to note that the distinction between supervised and unsupervised is typically used in the machine-learning framework with reference to the adopted training; conversely, in the context of parallel computing, using the term \"unsupervised\" remains misleading. Another service offered in GEP is based on the FASTVEL algorithm for generating differential interferograms or PSI-based mean displacement velocity maps [20]. Indicatively, such services allow processing a stack of 100 images in about a couple of days. It is worth mentioning that the exploitation at the user level of the implemented FASTVEL and P-SBASalgorithms can lead, in some cases, to results that might not be consistent [218]. Currently, the GEP services are available to the public according to a pay-per-use basis. This ESA initiative is valuable since it enables InSAR products exploitation for preliminary geo-hazard investigations; however, from the HPC perspective, it offers little insight into the actual computational resources being utilized and the performance of the relevant implementations. We now focus on the remarkable trend in the InSAR technique concerning wide-area deformation monitoring initiatives and the increasing of operational motion services (GMS) for dissemination [219]. In particular, the recently established European Ground Motion Service (EGMS), which is part of the Copernicus land monitoring service, deserves to be mentioned. As an example of national GMS based on Sentinel-1 data, we mention the InSAR products (based on PSI processing) with complete coverage of Norway, which is freely available through an open dissemination platform [220]. A similar national-scale approach was also followed in Germany using PSI techniques [221] and nationwide GMS for Denmark using SqueeSAR [219]. The deformation maps are distributed free of charge. Likewise, The FLATSIM (ForM@Ter LArge-scale multi-Temporal Sentinel-1 Interferometry service) service is based on the classical SBAS approach and is supported by the French Spatial Agency (CNES): it will allow multi-temporal InSAR processing of large continental areas interferograms, surface line-of-sight velocity, time series, and auxiliary data for the French scientific community [222].