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E-Learning for Geoinformation Science

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The internet and e-learning is now established in lifelong education. Not only universities are distributing additional training material but also companies are offering e-learning courses, webinars or podcasts for customer training more and more often.
IGG E-Learning
This podcast discusses the advantages of mobile learning, focussing on podcasts and RSS feeds.

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The City Geography Markup Language (CityGML)

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The City Geography Markup Language (CityGML) is an OpenGIS® Encoding Standard for the representation, storage and exchange of virtual 3D city and landscape models. This training course on CityGML has been developed and is operated by the Institute of Geodesy and Geoinformation Science, Berlin University of Technology (Germany).
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The main motivation for the development of City GML was not to create just another 3D exchange format, but has its motivation in the field of urban information modeling. 3D visualizations of spatial objects are employed in an increasing number of applications from the areas of (urban) planning, city marketing, tourism, and facility management. 3D city and landscape models reveal a high information potential for a variety of application fields.
The Geography Markup Language (GML) is the XML grammar defined by the Open Geospatial Consortium (OGC) to express geographical features. GML serves as a modeling language for geographic systems as well as an open interchange format for geographic transactions on the Internet. CityGML is an application schema of GML.
CityGML is a common information model for the representation of 3D urban objects. It defines the classes and relations for the most relevant topographic objects in cities and regional models with respect to their geometrical, topological, semantical and appearance properties. Included are generalization hierarchies between thematic classes, aggregations, relations between objects, and spatial properties. These thematic information go beyond graphic exchange formats and allow to employ virtual 3D city models for sophisticated analysis tasks in different application domains like simulations, urban data mining, facility management, and thematic inquiries.

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Planetary Robotic Vision Ground Processing

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PRoVisG is a EU FP7 funded collaborative project of European and US American institutions and businesses building a framework for planetary robotics ground processing. It will develop technology to better process and visualise existing and future planetary robotics mission data to enhance the exploitation of these data for research.
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In the future, planetary robotic missions are expected to focus on in-situ exploration of planetary surfaces and atmospheres. Such missions typically involve autonomous exploration tools, either surface (rovers) or aerial vehicles (balloons, aerobots etc.). The Planetary Robotics Vision Ground Processing (PROVISG) project, sponsored by the EU FP7 (European Union, 7th Framework Program) started in October 2008 and aims to build a framework for planetary robotics vision ground processing. This podcast will give an introduction on the topic of planetary exploration and where PRoVisG will fit into the overall picture.

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