TRAINING
INSPIRE Data Specifications advanced |
Source |
This training module has been developed within the context of the smeSpire project in 2014 (http://www.smespire.eu/). |
Ownership |
Author: Stijn Keyers – KU Leuven. The material is provided under Creative Commons Attribution Share-Alike License (http://creativecommons.org/licenses/by-sa/3.0/). |
Abstract |
One of the major goals of INSPIRE is to create harmonized spatial data sets that can be used seamlessly in cross-border applications. In order to reach this goal it is necessary to agree on common definitions for the different themes covered by INSPIRE. An approach has been agreed by all the INSPIRE stakeholders based on a series of international standards, i.e. the ISO 19100 series of geomatics standards. This common approach make it possible that spatial data can be transformed into the agreed specifications for use in cross-border contexts. The transformation of spatial data sets between existing data models and data specifications as defined in the Implementing Rules are the major challenge to reach interoperability and to make INSPIRE work. The training session gives an introduction to XML and GML and discusses the conceptual and operational aspects of transforming existing spatial data sets to INSPIRE specifications. It focuses on the schema matching and mapping as well as on the transformation itself. Some hands-on exercises on simple data sets provide the participants with some practical experience. |
Structure |
This seminar contains the following modules/parts:
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Learning outcomes |
After the training offer, the participant will be able to: - Read and understand UML diagrams for INSPIRE datasets |
Intended Audience |
This seminar aims at GI professionals and ICT professionals who need to understand and apply the process of transforming data conform the requirements of the INSPIRE rules and guidelines. |
Pre-requisites |
Prior knowledge: Basics of INSPIRE Data Specifications |
Language |
English |
Format |
Presentation. |
Expected workload |
Expected workload is 12 hours. |