Development of a New Framework for Distributed Processing of Geospatial Big Data

Geospatial technology is still facing a lack of “out of the box” distributed processing solutions which are suitable for the amount and heterogeneity of geodata, and particularly for use cases requiring a rapid response. Moreover, most of the current distributed computing frameworks have important limitations hindering the transparent and flexible control of processing (and/or storage) nodes and control of distribution of data chunks. We investigated the design of distributed processing systems and existing solutions related to Geospatial Big Data. This research area is highly dynamic in terms of new developments and the re-use of existing solutions (that is, the re-use of certain modules to implement further specific developments), with new implementations continuously emerging in areas such as disaster management, environmental monitoring and earth observation. The distributed processing of raster data sets is the focus of this paper, as we believe that the problem of raster data partitioning is far from trivial: a number of tiling and stitching requirements need to be addressed to be able to fulfil the needs of efficient image processing beyond pixel level. We attempt to compare the terms Big Data, Geospatial Big Data and the traditional Geospatial Data in order to clarify the typical differences, to compare them in terms of storage and processing backgrounds for different data representations and to categorize the common processing systems from the aspect of distributed raster processing. This clarification is necessary due to the fact that they behave differently on the processing side, and particular processing solutions need to be developed according to their characteristics. Furthermore, we compare parallel and distributed computing, taking into account the fact that these are used improperly in several cases. We also briefly assess the widely-known MapReduce paradigm in the context of geospatial applications. The second half of the article reports on a new processing framework initiative, currently at the concept and early development stages, which aims to be capable of processing raster, vector and point cloud data in a distributed IT ecosystem. The developed system is modular, has no limitations on programming language environment, and can execute scripts written in any development language (e.g. Python, R or C#)

Big Geospatial Data processing in the IQmulus Cloud

Remote sensing instruments are continuously evolving in terms of spatial, spectral and temporal resolutions and hence provide exponentially increasing amounts of raw data. These volumes increase significantly faster than computing speeds. All these techniques record lots of data, yet in different data models and representations; therefore, resulting datasets require harmonization and integration prior to deriving meaningful information from them. All in all, huge datasets are available but raw data is almost of no value if not processed, semantically enriched and quality checked. The derived information need to be transferred and published to all level of possible users (from decision makers to citizens). Up to now, there are only limited automatic procedures for this; thus, a wealth of information is latent in many datasets. This paper presents the first achievements of the IQmulus EU FP7 research and development project with respect to processing and analysis of big geospatial data in the context of flood and waterlogging detection

IQPC 2015 Track: Water Detection And Classification On Multi-Source Remote Sensing And Terrain Data

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Effect of the Equal Channel Angular Pressing on the Microstructure and Phase Composition of a 7xxx Series Al-Zn-Mg-Zr Alloy

A supersaturated Al-4.8%Zn-1.2%Mg-0.14%Zr (wt%) alloy was processed by the equal-channel angular pressing (ECAP) technique at room temperature in order to obtain an ultrafine-grained (UFG) microstructure having an average grain size of about 260 nm. The hardness and microstructural characteristics, such as the phase composition and precipitations of this UFG microstructure were studied using depth-sensing indentation (DSI), transmission electron microscopy (TEM), as well as non-isothermal scanning of differential scanning calorimetry (DSC), and compared to the properties of the un-deformed sample. Emphasis was placed on the effect of the UFG microstructure on the subsequent thermal processes in DSC measurements. It has been shown that the ECAP process resulted in not only an ultrafine-grained but also a strongly precipitated microstructure, leading to a hardness (2115 MPa) two and a half times higher than the initial hardness of the freshly quenched sample. Because of the significant changes in microstructure, ECAP has also a strong effect on the dissolution (endothermic) and precipitation (exothermic) processes during DSC measurements, where the dissolution and precipitation processes were quantitatively characterized by using experimentally determined specific enthalpies, ΔH and activation energies, Q

Investigation of the RF Sputtering Process and the Properties of Deposited Silicon Oxynitride Layers Under Varying Reactive Gas Conditions

In a single process run, an amorphous silicon oxynitride layer was grown, which includes the entire transition from oxide to nitride. The variation of the optical properties and the thickness of the layer was characterized by Spectroscopic Ellipsometry (SE) measurements, while the elemental composition was investigated by Energy Dispersive Spectroscopy (EDS). It was revealed that the refractive index of the layer at 632.8 nm is tunable in the 1.48–1.89 range by varying the oxygen partial pressure in the chamber. From the data of the composition of the layer, the typical physical parameters of the process were determined by applying the Berg model valid for reactive sputtering. In our modelling, a new approach was introduced, where the metallic Si target sputtered with a uniform nitrogen and variable oxygen gas flow was considered as an oxygen gas-sputtered SiN target. The layer growth method used in the present work and the revealed correlations between sputtering parameters, layer composition and refractive index, enable both the achievement of the desired optical properties of silicon oxynitride layers and the production of thin films with gradient refractive index for technology applications

Full-length genome sequence analysis of a Hungarian Porcine Reproductive and Respiratory Syndrome Virus isolated from severe respiratory disease

The authors report the isolation of a Type 1 PRRSV strain from a clinical outbreak of severe respiratory problems and high fever. Next generation sequencing was used to determine the complete genome sequence of the isolate (9625/2012). The virus belongs to a new branch within subtype 1, clade D, containing mostly Spanish sequences and shows highest similarity to PRRSV Olot/1991 and to the Amervac vaccine strain. SimPlot analysis performed with the available full-length genome sequences indicates no evidences of recombination. Mutation analysis of 9625/2012 and the most related strains revealed high proportion of amino acid substitutions in the putative neutralizing epitopes, suggesting an important role of the selective immune pressure in the evolution of PRRSV 9625/2012

Modification of the Hall-Petch Relationship for Submicron-Grained FCC Metals

Experimental data show that the conventional Hall-Petch relationship cannot be maintained in its original form for metals having submicrometer structures. We now propose a dislocation model which modifies the Hall-Patch relationship to provide a uniform description of the grain size strengthening of submicron-structured face-centered cubic (f.c.c.) metals and solid solution alloys