Spatial Data Infrastructures

As spatial data infrastructure (SDI) is called a complex network for the exchange of information and data with a spatial extend (spatial data), within the meaning of comparable to other infrastructure, such as transport networks or telecommunications are.

Components of a spatial data infrastructure are the spatial data itself, descriptive metadata, as well as a network of services and standards.

Providers and users take advantage of established rules, technical, organizational and legal compliant spatial data that can be searched and visualized in geoportals. The collection and processing of spatial data is done in geographic information systems (GIS), which is an important basis for the development of spatial data infrastructures in government, industry and academia.

The i3mainz supports all stakeholders in building your spatial data infrastructures, municipalities, state agencies, planning agencies, the real estate industry, supply and disposal operations, as well as in national and international level (INSPIRE).

Contact Person

Prof. Dr.-Ing. Hartmut Müller

Tel.: +49 6131-628-1438
Fax.: +49 6131-628-91438

Prof. Dr. phil. Kai-Christian Bruhn

Tel.: +49 6131-628-1433
Fax.: +49 6131-628-91433

Prof. Dr. Karl-Albrecht Klinge

Tel.: +49 (0) 6131-628-1434
Fax.: +49 (0) 6131-628-91434

Projects

other projects »

Publications

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i3mainz - Jahresbericht 2018

2019

PDF / RTF

Jahrebericht 2018

Im Jahresbericht werden die Projekte und Aktivitäten des i3mainz in komprimierter Form vorgestellt.

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Automatic Integration of Spatial Data into the Semantic Web

2017

RTF

WebIST 2017

For several years, many researchers tried to semantically integrate geospatial datasets into the semantic web. Although, there are many general means of integrating interconnected relational datasets (e.g. R2RML), importing schema-less relational geospatial data remains a major challenge in the semantic web community. In our project SemGIS we face significant importation challenges of schema-less geodatasets, in various data formats without relations to the semantic web. We therefore developed an automatic process of semantification for aforementioned data using among others the geometry of spatial objects. We combine Natural Language processing with geographic and semantic tools in order to extract semantic information of spatial data into a local ontology linked to existing semantic web resources. For our experiments, we used LinkedGeoData and Geonames ontologies to link semantic spatial information and compared links with DBpedia and Wikidata for other types of information. The aim of our experiments presented in this paper, is to examine the feasibility and limits of an automated integration of spatial data into a semantic knowledge base and to assess its correctness according to different open datasets. Other ways to link these open datasets have been applied and we used the different results for evaluating our automatic approach.

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Katastrophenmanagement: Die geflutete Stadt

2017

RTF

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Terrain difficulty as a relevant proxy for objectifying mobility patterns and economic behaviour in the Aurignacian of the Middle Danube region: the case of Stratzing (Austria)

2017

RTF

The Exploitation of Raw Materials in Prehistory: Sourcing, Processing and Distribution

This paper addresses the factors that conditioned the choices in lithic resource procurement for tool making at the Late Aurignacian site of Stratzing-Galgenberg (Austria), based on the lithic assemblage from the main area of the site. The raw materials used in the analysed assemblage are varied and partly relate to various local and non-local proveniences. The importance of non-local flint in the assemblage contradicts the distance decay model according to which the amount of a given raw material decreases with the increasing distance from its source. Drawing on the approach developed recently by Lucy Wilson, we examine the predictive ability of “source attractiveness” with respect to terrain difficulty and energy expenditure to understand why some sources were used more than others, using a Geographic Information System (GIS). Our results indicate that terrain difficulty and mobility costs matter and have a better predictive ability than Euclidean distance alone to explain assemblage variability in the Aurignacian of the Middle Danube region.

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Integration, quality assurance and usage of geospatial data with semantic tools

2017

RTF

gis.Science

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A Framework to Improve the Disaster Response Through a Knowledge-Based Multi-Agent System

2017

RTF

International Journal of Information Systems for Crisis Response and Management (IJISCRAM)

The disaster response still faces problems of collaboration due to lack of policies concerning the information exchange during the response. Moreover, plans are prepared to respond to a disaster, but drills to apply them are limited and do not allow to determine their efficiency and conflicts with other organizations. This paper presents a framework allowing for different organizations involving in the disaster response to assess their collaboration through its simulation using an explicit representation of their knowledge. This framework is based on a multi-agent system composed of three generic agent models to represent the organizational structure of disaster response. The decision-making about response actions is done through task decomposition and repartition. It is based reasoning on ontologies which provides an explicit trace of the response plans design and their execution. Such framework aims at identifying cooperation problems and testing strategies of information exchange to support the preparation of disaster response.

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Systemarchitektur eines mobilen Empfehlungssystems mit Echtzeitanalysen von Sensordaten für Asthmatiker

2016

RTF

Multikonferenz Wirtschaftsinformatik (MKWI)

Das Projekt ActOnAir verfolgt einen neuartigen Ansatz zur Erfassung, Kombination und Analyse persönlicher Belastungsinformationen und feinmaschiger Umweltdaten für Asthmatiker. Individuelle Handlungsalternativen werden identifiziert, bewertet und überwacht. Dieser Beitrag stellt Gestaltungsziele und Kernanforderungen für die zu entwickelnden Softwarekomponenten vor und beschreibt eine hierzu passende Systemarchitektur sowie deren prototypische Umsetzung.

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Ein Modell der Interoperabilität für GIS-Anwender im Rahmen von INSPIRE

2016

RTF

AVN - Allgemeine Vermessungsnachrichten, Ausgabe 4-5/2016

Mit INSPIRE – der Geodateninfrastruktur der Europäischen Gemeinschaft – wird das Ziel verfolgt, umweltrelevante und harmonisierte Geodatenbestände für eine nachhaltige europäische Umweltpolitik und zur allgemeinen Bereitstellung für die Bürger der EU verfügbar zu machen. Die Konzeption und Umsetzung von Diensten der Behörden in Europa muss auf der Grundlage von vernetzten Datenbanken erfolgen. Zur Gewährleistung einer Interoperabilität zwischen den verschiedenen Ebenen und Systemen ist es zwingend notwendig, dass auf den verschiedenen Ebenen die Behörden und Systeme über gemeinsame Standards kommunizieren. Dabei sind sowohl semantische, syntaktische und organisatorische Rahmenbedingungen der Verwaltungen zu berücksichtigen. Diese spiegeln sich vor allem in den vorhandenen E-Government-Aktivitäten, Standardisierungen und Geoinformationssystemen (GIS) der Verwaltung wider. Zur Realisierung einer Geodateninfrastruktur in der Europäischen Gemeinschaft sollen die Mitgliedsstaaten ihre bereits vorhandenen Geodatensätze und -dienste stufenweise interoperabel verfügbar machen. Damit soll die Entscheidungsfindung in Bezug auf politische Konzepte und Maßnahmen, die direkte oder indirekte Auswirkungen auf die Umwelt haben können, unterstützt werden. Jedoch haben die gewachsenen Verwaltungsstrukturen in Städten und Landkreisen mit ihren jeweiligen Besonderheiten zu heterogenen Datenbeständen geführt. In dem entwickelten E-Government-Modell einer Geodateninfrastruktur (Geo-Government) kann Interoperabilität auf allen Ebenen nachgewiesen werden.

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