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INSPIRing Geophysics

Lucie Kondrová, Dana Čápová, Olga Moravcová, Roman Kujal and Eva Hudečková

(Submission #272)


Abstract

CzechGeo / EPOS is the system of geophysical observations performed by eight geoscience institutions of the Czech Republic (including research institutes, universities and the geological survey organisation). It includes maintenance and development of permanent observatories, local stations and mobile stations, as well as the databases and networks to provide geophysical data for public or for research purposes. Apart from being provided through already established and functional global networks, geophysical data is part of the INSPIRE theme Geology and as such will be available in a harmonized way through standardized web services. The Czech Geological Survey is performing the data analysis for CzechGeo regarding the relevance of its data to INSPIRE, and because of its rich experience with provision of internationally interoperable data and services will focus on the provision of the CzechGeo data for INSPIRE. Access to existing relevant information is essential for the scientific research, namely when aimed to the prediction and mitigation of landslides, subsidence, earthquakes, flooding and pollution. In 2016, almost 40 different data sets were identified as partly or completely relevant for INSPIRE. Data is available in different forms, such as structured data in databases, unstructured data, paper documents and primary documentation. The works in 2017 are focused on the metadata description of all the available geophysical data, analysis of INSPIRE codelists for geophysical data, initiation of the data harmonization process, and analysis of possibilities of 3D models provision, that are also included in the INSPIRE Geology theme as its extension.

Categories

Topic Area:  [2.8] Innovation and technologies for spatial data collection, processing and integration in spatial data infrastructures (for example; Galileo/EGNOSS, Copernicus data and services, sensor web, Internet of Things, Big Data analytics)
Abstract Type:  Oral Presentation

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