Artificial intelligence-based solution to estimate the spatial accuracy of volunteered geographic data

Mohammadi, Nazila; Malek, M. R.

doi:10.1080/14498596.2014.927337

Cited by 19 publications

(16 citation statements)

References 34 publications

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“…Similarly, Jilani and Corcoran () extracted geometrical and topological properties of OSM street network data that are representative of their semantic class, to infer the “road class” from the new data. Finally, Mohammadi and Malek () estimated the positional accuracy of OSM data without corresponding reference data by extracting patterns from OSM data that have corresponding reference data.…”

Section: Taxonomy Of Quality Assessment Methodsmentioning

confidence: 99%

“…These quality elements include completeness, positional accuracy, and thematic accuracy, among others. Although these elements can be applied to measure the quality of CGI, this type of information has particular features which make assessing its quality different from traditional geographic data (Mohammadi & Malek, ). Hence, researchers have added new elements to assist in assessing the quality of CGI (e.g., trust), or made new definitions for existing quality elements (Fan, Zipf, Fu, & Neis, ; Girres & Touya, ).…”

Section: Quality Of Crowdsourced Geographic Informationmentioning

confidence: 99%

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A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information

Degrossi

Albuquerque

Rocha

et al. 2018

Transactions in GIS

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The growing use of crowdsourced geographic information (CGI) has prompted the employment of several methods for assessing information quality, which are aimed at addressing concerns on the lack of quality of the information provided by non‐experts. In this work, we propose a taxonomy of methods for assessing the quality of CGI when no reference data are available, which is likely to be the most common situation in practice. Our taxonomy includes 11 quality assessment methods that were identified by means of a systematic literature review. These methods are described in detail, including their main characteristics and limitations. This taxonomy not only provides a systematic and comprehensive account of the existing set of methods for CGI quality assessment, but also enables researchers working on the quality of CGI in various sources (e.g., social media, crowd sensing, collaborative mapping) to learn from each other, thus opening up avenues for future work that combines and extends existing methods into new application areas and domains.

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Section: Taxonomy Of Quality Assessment Methodsmentioning

confidence: 99%

Section: Quality Of Crowdsourced Geographic Informationmentioning

confidence: 99%

A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information

Degrossi

Albuquerque

Rocha

et al. 2018

Transactions in GIS

View full text Add to dashboard Cite

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“…There exist multiple approaches for the assessment of the quality of VGI data which can be described by quality measures and quality indicators (Senaratne et al, 2017). Basically, a distinction is made between two different approaches: measures based on a comparison of VGI data with external data sources and intrinsic indicators (Antoniou and Skopeliti, 2015;Mohammadi, and Malek, 2015). Different measures for the quality of VGI data were specified and used if authoritative data is available: e.g.…”

Section: Related Workmentioning

confidence: 99%

“…For unification there is only a recommended procedure describing how to collect the data correctly. Thus the question of data quality (Zielstra and Zipf, 2010;Koukoletsos et al, 2011;Fan et al, 2014;Antoniou and Skopeliti, 2015;Mohammadi and Malek, 2015;Senaratne et al, 2017; is inevitable. The principle of data collection entails high risks and raises doubts about the quality of the data.…”

Section: Introductionmentioning

confidence: 99%

Database-Supported Change Analysis and Quality Evaluation of Openstreetmap Data

Martini

Kuper

Breunig

2019

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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<p><strong>Abstract.</strong> A significant advantage of OpenStreetMap data is its up-to-dateness. However, for rural and city planning, it is also of importance to access historical data and to compare the changes between new and old versions of the same area. This paper first introduces into a differentiated classification of changes on OpenStreetMap data sets. Then a methodology for an automated database-supported analysis of changes is presented. Beyond the information already provided from the OpenStreetMap server, we present a more detailed analysis with derived information. Based on this approach it is possible to identify objects with attributive or geometric changes and to find out how they exactly differ from their previous versions. The analysis shows in which regions mappers were active during a certain time interval. Furthermore, a time based approach based on various parameters to determine the quality of the data is presented. It provides a guideline of data quality and works without any reference data. Therefore, an indication about the development of OpenStreetMap in terms of completeness and correctness of the data in different regions is given. Finally, a conclusion and an outlook on open research questions are presented.</p>

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“…One of the most important challenges of VGI development is uncertainty mentioned by many researchers who have considered it from different aspects (Allingham, 2014;Barron et al, 2013;Elwood et al, 2013;Mohammadi and Malek, 2015). The research conducted on VGI uncertainty have mostly measured the uncertainty of VGI datasets (Vandecasteele, and Devillers, 2015), 2) or concerned with spatial aspect of the data rather than non-spatial aspect (Mülligann et al, 2011;Mooney and Corcoran, 2012).…”

Section: Introductionmentioning

confidence: 99%

You Describe It, I Will Name It: An Approach to Alleviate the Effect of Users’ Semantics in Assigning Tags to Features in Vgi

Pazoky

Karimipour

Hakimpour

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:As an important factor of VGI quality, this paper focuses on uncertainty arisen in assigning tags to features by VGI users. The VGI portals ask their users to assign (or tag) one or more data types to features, from a set of pre-defined types, whose meanings may be vague for the user, or distinctions between some of them are not clear, i.e. depend on the users' semantics. This research believes such uncertainties are the results of perceptual issues arising in serial communication between the system and the user. We categorize the problem, and then utilize semantic modelling to reduce such uncertainties. A hierarchy of feature types is produced. At each step, users are asked a simple question with clear distinct answers, which gradually directs the user to the right type. We will describe the approach and present the initial results for the hierarchy produced for major linear features of OpenStreetMap.

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Artificial intelligence-based solution to estimate the spatial accuracy of volunteered geographic data

Cited by 19 publications

References 34 publications

A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information

A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information

Database-Supported Change Analysis and Quality Evaluation of Openstreetmap Data

You Describe It, I Will Name It: An Approach to Alleviate the Effect of Users’ Semantics in Assigning Tags to Features in Vgi

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