Institut für Raumbezogene Informations- und Messtechnik
Hochschule Mainz - University of Applied Sciences

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 MĂĽller

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

Kai-Christian Bruhn

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

Messages

Projects

Das Projekt verfolgt das Ziel, verschiedene Prototypen fĂĽr digitale Anwendungen zu entwickeln. Diese sollen fĂĽr Projekte der Hochschule Mainz ebenso verfĂĽgbar sein, wie fĂĽr externe...
Für ältere Menschen ist der Weg zum Supermarkt, zur Apotheke oder zum Arzt in ländlichen Regionen oft zu weit. Von Versorgungsengpässen sind Ortskerne von strukturschwachen Dörfern...

Publications

An Overview of Approaches for automated intelligent Building Information Modeling

2020

PDF / RTF

FIG Working Week 2020
The digitalisation of architecture, engineering and construction (AEC) industry is gaining much attention especially through Building Information Modeling (BIM). While the use of IT-supported planning and construction processes is required for new building projects, the creation of BIM-valid data for existing buildings is currently inefficient. Academic and industry are spending a lot of effort into research for flexible methods to measure as-built conditions. The automated processing of the resulting 3D point cloud into BIM-valid 3D CAD models using intelligent software approaches is another major research.
This paper presents an overview of data acquisition techniques and 3D point cloud processing approaches regarding BIM for existing buildings, while identifying challenges and looking ahead for future research. To optimise decision making with respect to socially relevant issues, BIM as an instrument can revolutionise the AEC industry and provide the database for smart city applications.

i3mainz - Jahresbericht 2018

2019

PDF / RTF

Jahrebericht 2018

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


Modélisation sémantique et logique pour une simulation multi-agent dans le contexte de gestion de catastrophe

2019

RTF

Spatial Analysis and Geomatics (SAGEO) 2019

Disaster management requires both individual and collaborative preparedness among the various stakeholders.
Collaborative exercises aim to train stakeholders to apply the plans prepared and to identify potential problems and areas for improvement. As these exercises are costly, computer simulation is an interesting tool to evaluate preparation through a wider variety of contexts.
However, research on simulation and disaster management focuses on a particular problem rather than on the overall assessment of the plans prepared. This limitation is explained by the challenge of creating a simulation model that can represent and adapt to a wide variety of plans from various disciplines.
The work presented in this paper addresses this challenge by adapting the simulation model based on disaster management information and plans integrated into a knowledge base. The simulation model created is then automatically programmed to perform simulation experiments to improve action plans.
The results of the experiments are analyzed in order to generate new knowledge and know-how to enrich disaster management plans in a virtuous cycle.
This paper presents a proof of concept on the French national Novi plan, for which simulation experiments have made it possible to know the impact of the distribution of doctors on the application of the plan as well as to identify their distribution.


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.


Katastrophenmanagement: Die geflutete Stadt

2017

RTF

ns








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.


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

2017

RTF

gis.Science








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.