Big Geospatial Data processing in the IQmulus Cloud

Olasz, Angéla and Thai Nguyen, Binh and Giachetta, Roberto and Kristóf, Dániel (2016) Big Geospatial Data processing in the IQmulus Cloud. In: 11th International Symposium on Applied Informatics and Related Areas (AIS 2016). Óbudai Egyetem, Budapest, pp. 68-74.

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Abstract

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.

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