Spatial Data Quality Assessment and Documentation

Nowadays an ever-increasing number of applications require complete and up-to-date spatial data, in particular maps. However, mapping is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete. In this context crowd-sourced maps have the potential to provide a complete, up-to-date, and free representation of our world. However, the proliferation of such maps largely remains limited due to concerns about their data quality. While most of the current data quality assessment mechanisms for such maps require referencing to authoritative maps, we argue that such referencing of a crowd-sourced spatial database is ineffective. Instead we focus on the use of machine learning techniques that we believe have the potential to not only allow the assessment but also to recommend the improvement of the quality of crowd-sourced maps without referencing to external databases. This chapter gives an overview of these approaches.

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Traditional vs. Machine-Learning Techniques for OSM Quality Assessment

Advances in Geospatial Technologies - Volunteered Geographic Information and the Future of Geospatial Data ◽

10.4018/978-1-5225-2446-5.ch003 ◽

2017 ◽

pp. 47-64

Author(s):

Musfira Jilani ◽

Michela Bertolotto ◽

Padraig Corcoran ◽

Amerah Alghanim

Keyword(s):

Machine Learning ◽

Data Quality ◽

Quality Assessment ◽

Spatial Data ◽

Current Data ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Data Quality Assessment ◽

Expensive Process

Nowadays an ever-increasing number of applications require complete and up-to-date spatial data, in particular maps. However, mapping is an expensive process and the vastness and dynamics of our world usually render centralized and authoritative maps outdated and incomplete. In this context crowd-sourced maps have the potential to provide a complete, up-to-date, and free representation of our world. However, the proliferation of such maps largely remains limited due to concerns about their data quality. While most of the current data quality assessment mechanisms for such maps require referencing to authoritative maps, we argue that such referencing of a crowd-sourced spatial database is ineffective. Instead we focus on the use of machine learning techniques that we believe have the potential to not only allow the assessment but also to recommend the improvement of the quality of crowd-sourced maps without referencing to external databases. This chapter gives an overview of these approaches.

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WEB SERVICE FOR POSITIONAL QUALITY ASSESSMENT: THE WPS TIER

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-3-w5-257-2015 ◽

2015 ◽

Vol II-3/W5 ◽

pp. 257-262 ◽

Cited By ~ 2

Author(s):

E. M. A. Xavier ◽

F. J. Ariza-López ◽

M. A. Ureña-Cámara

Keyword(s):

Quality Assessment ◽

Spatial Data ◽

Positional Accuracy ◽

Advantages And Disadvantages ◽

Spatial Data Quality ◽

Data Quality Assessment ◽

Specific Assessment ◽

Accuracy Assessments ◽

Assessment Procedures

In the field of spatial data every day we have more and more information available, but we still have little or very little information about the quality of spatial data. We consider that the automation of the spatial data quality assessment is a true need for the geomatic sector, and that automation is possible by means of web processing services (WPS), and the application of specific assessment procedures. In this paper we propose and develop a WPS tier centered on the automation of the positional quality assessment. An experiment using the NSSDA positional accuracy method is presented. The experiment involves the uploading by the client of two datasets (reference and evaluation data). The processing is to determine homologous pairs of points (by distance) and calculate the value of positional accuracy under the NSSDA standard. The process generates a small report that is sent to the client. From our experiment, we reached some conclusions on the advantages and disadvantages of WPSs when applied to the automation of spatial data accuracy assessments.

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SDQO AND SFO, ONTOLOGIES FOR SPATIAL DATA QUALITY ASSESSMENT

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1275-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1275-1280 ◽

Cited By ~ 1

Author(s):

C. Yilmaz ◽

C. Comert ◽

D. Yildirim

Keyword(s):

Quality Management ◽

Semantic Web ◽

Data Quality ◽

Quality Assessment ◽

Spatial Data ◽

Assessment Process ◽

Rule Based ◽

Ontology Language ◽

Spatial Data Quality ◽

Domain Independent

<p><strong>Abstract.</strong> Spatial quality assessment is based on the conformance of data to its specifications or fitness for users’ purpose. These specifications and the users’ purposes include the rules and constraints that a dataset should comply with. Assessing the compliance of data to the rules is still an active research subject and rule-based approach is the common method. For the efficient rule-based system implementation, it is desired to automate assessment process with a domain-independent and web-based approach. Reasoning capability and re-usability of semantic web components are expected to promote efficient implementation. In literature, many domains such as agriculture, music, Linked Data and geospatial domain etc. apply ontology-based methods for quality management. There is a need to model geospatial quality concepts and rules in a domain-independent way to automate the quality management process. In our model of rule formalism, we use Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL). We devise two types of ontologies. These are; the specification ontologies (SfO) and the Spatial Data Quality Ontology (SDQO). SfO is to be created by domain experts/users to define rules according to specifications. SDQO is responsible with quality assessment; it is domain independent and makes assessment based on the rules defined by any SfO for the related domain. The quality elements are domain and toposemantic consistency that assessed by SWRL. In this paper, the design considerations of the ontologies for quality assessment are explained with an example.</p>

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