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Multi-decadal data sets are already available from various types of EO sensors, but their effective exploitation is hindered by the lack of data centres which offer dedicated EO processing chains and high-performance processing (HPC) capabilities. Recognizing this need, TU Wien founded the EODC Earth Observation Data Centre for Water Resources Monitoring which aims at providing an independent science-driven platform that is transparent for its users and offers a high diversity and flexibility in terms of data sets and algorithms used.

We present an efficient Cloud Computing (CC) implementation of the Parallel Small BAseline Subset (P-SBAS) algorithm, which is an advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) technique for the generation of Earth surface displacement time series through distributed computing infrastructures. The rationale of our approach consists in properly distributing the large data volumes and the processing tasks involved in the P-SBAS chain among the available (virtual and/or physical) computing nodes of the CC infrastructure, so that each one of these elements can concurrently work on data that are physically stored on its own local volume. To do this, both an ad hoc management of the data flow and an appropriate scheduling of the parallel jobs have been also implemented to properly handle the high complexity of the P-SBAS workflow. The proposed solution allows minimizing the overall data transfer and network load, thus improving the P-SBAS efficiency and scalability within the exploited CC environments. The presented P-SBAS implementation has been extensively validated through two experimental analyses, which have been carried out by exploiting the Amazon Web Services (AWS) Elastic Cloud Compute (EC2) resources. The former analysis involves the processing of a large (128 SAR images) COSMO-SkyMed dataset, which has been performed by exploiting up to 64 computing nodes, and is aimed at demonstrating the P-SBAS scalable performances. The latter allows us to show the P-SBAS capability to generate DInSAR results at a regional scale (150 000 km2 in Southern California) in a very short time (about 9 h), by simultaneously processing 18 ENVISAT frames that correspond to a total of 741 SAR images, exploiting in parallel 144 AWS computing nodes. The presented results confirm the effectiveness of the proposed P-SBAS CC solution, which may contribute to further extend the frontiers of the DInSAR investigation at a very large scale.

One of the major advantages of the Sentinel-1 data is its capability to provide very high spatio-temporal coverage allowing the mapping of large areas as well as creation of dense time-series of the Sentinel-1 acquisitions. The SGRT software developed at TU Wien aims at automated processing of Sentinel-1 data for global and regional products. The first step of the processing consists of the Sentinel-1 data geocoding with the help of S1TBX software and their resampling to a common grid. These resampled images serve as an input for the product derivation. Thus, it is very important to select the most reliable processing settings and assess the geocoding uncertainty for both backscatter and projected local incidence angle images. Within this study, selection of Sentinel-1 acquisitions over 3 test areas in Europe were processed manually in the S1TBX software, testing multiple software versions, processing settings and digital elevation models (DEM) and the accuracy of the resulting geocoded images were assessed. Secondly, all available Sentinel-1 data over the areas were processed using selected settings and detailed quality check was performed. Overall, strong influence of the used DEM on the geocoding quality was confirmed with differences up to 80 meters in areas with higher terrain variations. In flat areas, the geocoding accuracy of backscatter images was overall good, with observed shifts between 0 and 30m. Larger systematic shifts were identified in case of projected local incidence angle images. These results encourage the automated processing of large volumes of Sentinel-1 data.

We present a case study on the migration to a Cloud Computing environment of the advanced differential synthetic aperture radar interferometry (DInSAR) technique, referred to as Small BAseline Subset (SBAS), which is widely used for the investigation of Earth surface deformation phenomena. In particular, we focus on the SBAS parallel algorithmic solution, namely P-SBAS, that allows the production of mean deformation velocity maps and the corresponding displacement time-series from a temporal sequence of radar images by exploiting distributed computing architectures. The Cloud migration is carried out by encapsulating the overall P-SBAS application in virtual machines running on the Cloud; moreover, the Cloud resources provisioning and configuration phases are implemented in an automatic way. Such an approach allows us to preserve the P-SBAS parallelization strategy and to straightforwardly evaluate its performance within a Cloud environment by comparing it with those achieved on a HPC in-house cluster. The results we present were achieved by using the Amazon Elastic Compute Cloud (EC2) of the Amazon Web Services (AWS) to process SAR datasets collected by the ENVISAT satellite and show that, thanks to the Cloud resources availability and flexibility, large DInSAR data volumes can be processed through the P-SBAS algorithm in short time frames and at reduced costs. As a case study, the mean deformation velocity map of the southern California area has been generated by processing 172 ENVISAT images. By exploiting 32 EC2 instances this processing took less than 17 hours to complete, with a cost of USD 850. Considering the available PB-scale archives of SAR data and the upcoming huge SAR data flow relevant to the recently launched (April 2014) Sentinel-1A and the forthcoming Sentinel-1B satellites, the exploitation of Cloud Computing solutions is particularly relevant because of the possibility to provide Cloud-based multi-user services allowing worldwide scientists to quickly process SAR data and to manage and access the achieved DInSAR results.

Purpose: There is ongoing debate about whether testing low-risk genes at multiple loci will be useful in clinical care and public health. We investigated the usefulness of multiple genetic testing using simulated data. Methods: Usefulness was evaluated by the area under the receiver-operating characteristic curve (AUC), which indicates the accuracy of genetic profiling in discriminating between future patients and nonpatients. The AUC was investigated in relation to the number of genes assumed to be involved, the risk allele frequency, the odds ratio of the risk genotypes, and to the proportion of variance explained by genetic factors as an approximation of the heritability of the disease. Results: We demonstrated that a high (AUC > 0.80) to excellent discriminative accuracy (AUC > 0.95) can be obtained by simultaneously testing multiple susceptibility genes. A higher discriminative accuracy is obtained when genetic factors play a larger role in the disease, as indicated by the proportion of explained variance. The maximum discriminative accuracy of future genetic profiling can be estimated at present from the heritability and prevalence of disease. Conclusions: Genetic profiling may have the potential to identify individuals at higher risk of disease depending on the prevalence and heritability of the disease.

We present in this work a first performance assessment of the Parallel Small BAseline Subset (P-SBAS) algorithm, for the generation of Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) deformation maps and time series, which has been migrated to a Cloud Computing (CC) environment. In particular, we investigate the scalable performances of the P-SBAS algorithm by processing a selected ENVISAT ASAR image time series, which we use as a benchmark, and by exploiting the Amazon Web Services (AWS) CC platform. The presented analysis shows a very good match between the theoretical and experimental P-SBAS performances achieved within the CC environment. Moreover, the obtained results demonstrate that the implemented P-SBAS Cloud migration is able to process ENVISAT SAR image time series in short times (less than 7 h) and at low costs (about USD 200). The P-SBAS Cloud scalable performances are also compared to those achieved by exploiting an in-house High Performance Computing (HPC) cluster, showing that nearly no overhead is introduced by the presented Cloud solution. As a further outcome, the performed analysis allows us to identify the major bottlenecks that can hamper the P-SBAS performances within a CC environment, in the perspective of processing very huge SAR data flows such as those coming from the existing COSMO-SkyMed or the upcoming SENTINEL-1 constellation. This work represents a relevant step toward the challenging Earth Observation scenario focused on the joint exploitation of advanced DInSAR techniques and CC environments for the massive processing of Big SAR Data.

The aim of this paper is to design a novel parallel computing solution for the processing chain implementing the Small BAseline Subset (SBAS) Differential SAR Interferometry (DInSAR) technique. The proposed parallel solution (P-SBAS) is based on a dual-level parallelization approach and encompasses combined parallelization strategies, which are fully discussed in this paper. Moreover, the main methodological aspects of the proposed approach and their implications are also addressed. Finally, an experimental analysis, aimed at quantitatively evaluating the computational efficiency of the implemented parallel prototype, with respect to appropriate metrics, has been carried out on real data; this analysis confirms the effectiveness of the proposed parallel computing solution. In the current scenario, characterized by huge SAR archives relevant to the present and future SAR missions, the P-SBAS processing chain can play a key role to effectively exploit these big data volumes for the comprehension of the surface deformation dynamics of large areas of Earth.

This paper proposes a parallel model for the Differential Interferometry Synthetic Aperture Radar approach referred to as Small BAseline Subset (SBAS) algorithm. This new computational model has been designed to be specifically exploited within the emerging cloud computing environments. An experimental analysis, involving two different case studies, has been carried out to demonstrate the effectiveness of the methodology. The major novelty of the proposed Parallel SBAS (P-SBAS) model consists in the capability of processing large SAR data sets in reasonable time-frames. This key feature may be of great impact not only for hazard monitoring and risk mitigation activities but also for data sharing and knowledge spreading within the scientific community.

The advanced Differential SAR Interferometers (DInSAR) methodologies are widely used for the investigation of Earth's surface deformation phenomena. In particular, the advanced DInSAR approach referred to as Small Baseline Subset (SBAS) technique is able to produce deformation velocity maps and the corresponding displacement time-series from a temporal sequence of space borne SAR acquisitions. Considering the already huge SAR data archives as well the upcoming massive data flow coming from the SENTINEL satellite constellation, cloud computing can be a valid solution to carry out DInSAR analyses thanks to its scalability and flexibility features. In this paper, the focus is given on the migration of the whole parallel version of the SBAS technique, namely P-SBAS, to a cloud environment by taking into account different parameters that influence processing time. Experimental tests that have been performed using both private and public cloud are also presented.