VGI_SLAM

Deriving Spatial Data from Volunteered Geographic Information

Coordinatore	UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN    more  Organization address address: BELFIELD city: DUBLIN postcode: 4 contact info Titolo: Mr. Nome: Donal Cognome: Doolan Email: send email Telefono: +353 1 716 1656 Fax: +353 1 716 1216
Nazionalità Coordinatore	Ireland [IE]
Totale costo	177˙631 €
EC contributo	177˙631 €
Programma	FP7-PEOPLE Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
Code Call	FP7-PEOPLE-2012-IOF
Funding Scheme	MC-IOF
Anno di inizio	2013
Periodo (anno-mese-giorno)	2013-09-01   -   2015-08-31

 Partecipanti

#	participant	country	role	EC contrib. [€]
1	UNIVERSITY COLLEGE DUBLIN, NATIONAL UNIVERSITY OF IRELAND, DUBLIN  Organization address address: BELFIELD city: DUBLIN postcode: 4 contact info Titolo: Mr. Nome: Donal Cognome: Doolan Email: send email Telefono: +353 1 716 1656 Fax: +353 1 716 1216	IE (DUBLIN)	coordinator	177˙631.20

Mappa

 Word cloud

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 Obiettivo del progetto (Objective)

'Recent years have seen the widespread engagement of large numbers of private citizens in the creation of spatial data. These contributors, commonly referred to as “citizen sensors”, often have no formal qualifications in performing a task which was traditionally reserved to official mapping agencies. This paradigm of crowd sourced spatial data is commonly referred to as Volunteered Geographic Information (VGI) (Goodchild, 2007). The greatest concern when using any form of spatial data is its quality. Therefore the data derived from VGI should be of the highest possible quality and it is necessary to be able to make statements about this quality. These tasks are complicated by the fact that VGI may be captured with inaccurate devices, lacking detail and in some cases inconsistent. Consequently there exists a degree of uncertainty associated with VGI which must be considered if one is to derive accurate spatial data. The overall objective of the proposed research project is the development of new methodologies for deriving spatial data from VGI using a probabilistic formulation. We proposed to draw heavily from methodologies developed in the domain of robotics and specifically the sub-domain of Simultaneous Localization And Mapping (SLAM). Dr. Corcoran has much previous research experience in VGI and SLAM, and therefore is a suitable candidate to carry out the project in question. Prof. Leonard of Massachusetts Institute of Technology (MIT) and Dr. Bertolotto of University College Dublin (UCD) are experts in the areas of robotics and geographical information science respectively. Their institutes therefore represent suitable hosts for Dr. Corcoran. The result of this fellowship will be a significant and long lasting transfer of knowledge and skills from MIT to UCD.'

Introduzione (Teaser)

There is a rise in the general public population gathering data that identifies the location, size and shape of an object on Earth, such as a building, mountain, lake or road. An EU initiative is investigating the drawbacks of unqualified mapping of data and proposing solutions.

Descrizione progetto (Article)

More than ever before, citizens are harnessing web tools to create, collect and disseminate geographic data. This growth in volunteered geographic information (VGI) can lead to the inclusion of subjective or emotional data by people with no formal training. The quality and reliability of VGI techniques is a topic of much debate among government agencies and private industry responsible for accessing, manipulating or analysing spatial data via geographic information systems.

To address the issue, the EU-funded 'Deriving spatial data from volunteered geographic information' (VGI_SLAM) project is designing new methodologies to supply first-rate spatial data from VGI.

To better understand what influences the quality of spatial data, project members began by examining both VGI and traditional mapping carried out by the geospatial community. They found that quality can be improved by automating both mapping processes, thus reducing the amount of labour required to complete geospatial information tasks.

Project partners are developing a methodology to automate the procedure for adding semantic data to street networks generated by a free and editable global map. To achieve this, they used a Boston street network supplied by the popular collaborative mapping wiki.

VGI_SLAM envisions cost-effective and superior quality data for the geospatial community and citizens alike. The project will provide ways to benefit from user-generated geospatial content and crowdsourcing of high-quality geospatial data sets.