Multi-granular Street Network Representation towards Quality Assessment of OpenStreetMap Data

Jilani, Musfira; Corcoran, Padraig; Bertolotto, Michela

doi:10.1145/2533828.2533833

Cited by 21 publications

(13 citation statements)

References 12 publications

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“…Corcoran and Mooney () have described a method to assess the topological properties of OSM data. Jilani, Corcoran, and Bertolotto () have presented a multi‐granular representation of street networks. They said that it can be used to assess data quality based on geometrical and morphological characteristics.…”

Section: Related Workmentioning

confidence: 99%

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data

Sehra

Singh

et al. 2019

Transactions in GIS

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OpenStreetMap (OSM) produces a huge amount of labeled spatial data, but its quality has always been a deep concern.Numerous quality issues have been discussed in the vast literature, while the fitness of OSM for road navigability is only partly explored. Navigability depends on logical consistency, which focuses on the existence of logical contradictions within a data set. Researchers have discussed the insufficiency of established methods and the lack of a computational paradigm to assess the quality of the OSM data.To address the research gaps, the current work extended the capabilities of the Quantum GIS Processing Toolbox for assessment of spatial data. The models and scripts developed are able to assess logical consistency based on geographical topological consistency, semantic information, and morphological consistency. The established and proxy indicators are selected for measuring the logical consistency of OSM data for navigability. For empirical validation, OSM Punjab data are compared with authoritative data from HERE (proprietary) and the Remote Sensing Centre (RSC), Punjab, India. The results conclude that even the proprietary road data sets are not free from logical inconsistencies and data contributed by the masses are credible and navigable. OSM has produced better results than the RSC, but needs more crowd contributions to improve its quality. K E Y W O R D SLogical consistency, OpenStreetMap, PyQGIS, QGIS | 45 SEHRA Et Al. | INTRODUC TI ONThe technological stack of Web 2.0 has enabled the voluminous production of crowdsourced data and, in particular, geospatial data. Volunteered geographic information (VGI), supported by the technological stack of Web 2.0 and contributed by the crowd, offers an alternative intuitive method for the collection of geographic information through volunteering at little cost. VGI produces highly diversified, elaborated, topical, and contextualized spatial data. Probably one of the best-known examples of VGI is the OpenStreetMap (OSM) project, as OSM data are free to use and reduce information gaps for the availability of recent map data.During the last decade, OSM has gained maturity and numerous articles have been published. Furthermore, a research drift toward analysis and fitness for use of OSM in various application domains has been witnessed (Sehra, Singh & Rai, 2013. The OSM project produces a huge amount of labeled spatial data contributed by users. Barrington-Leigh and Millard-Ball (2017) stated that more than 40% of countries have 83% complete OSM data (fully mapped street network), among them are several developed countries.

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Section: Related Workmentioning

confidence: 99%

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data

Sehra

Singh

et al. 2019

Transactions in GIS

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“…• Hausdorff Distance [79,136] • Buffer Overlay [7,77,78,84,97,185,197,198] • Buffer Overlay Statistics [93,199] • Photogrammetric approach [92] • Multi-granular graph based approach [95] • Intrinsic method [103,104],…”

Section: Quality Indicator Methods Usedmentioning

confidence: 99%

Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

Sehra

Singh

2017

IJGI

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OpenStreetMap (OSM), based on collaborative mapping, has become a subject of great interest to the academic community, resulting in a considerable body of literature produced by many researchers. In this paper, we use Latent Semantic Analysis (LSA) to help identify the emerging research trends in OSM. An extensive corpus of 485 academic abstracts of papers published during the period 2007-2016 was used. Five core research areas and fifty research trends were identified in this study. In addition, potential future research directions have been provided to aid geospatial information scientists, technologists and researchers in undertaking future OSM research.

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“…Therefore, it is necessary to construct and link both representations. An approach for achieving this was presented by Jilani et al [7] in the form of a multi-granular representation for street networks. This is essentially a two-layered representation which combines both the primal and the dual forms of street network representations.…”

Section: Street Network Representationmentioning

confidence: 99%

“…Therefore, the measurement of such characteristics requires two steps: firstly, determining the sets which are implicitly represented in the street network and secondly, measuring the characteristics of these sets that allow us to differentiate between different semantic classes. In order to extract connected sets of streets of uniform class we construct a multi-granular representation of the street network [7]. Subsequently we extract characteristics from these sets which allow us to discriminate between different classes.…”

Section: Introductionmentioning

confidence: 99%

Automated highway tag assessment of OpenStreetMap road networks

Jilani

Corcoran

Bertolotto

2014

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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OpenStreetMap (OSM) has been demonstrated to be a valuable source of spatial data in the context of many applications. However concerns still exist regarding the quality of such data and this has limited the proliferation of its use. Consequently much research has been invested in the development of methods for assessing and/or improving the quality of OSM data. However most of these methods require ground-truth data, which, in many cases, may not be available. In this paper we present a novel solution for OSM data quality assessment that does not require ground-truth data. We consider the semantic accuracy of OSM street network data, and in particular, the associated semantic class (road class) information. A machine learning model is proposed that learns the geometrical and topological characteristics of different semantic classes of streets. This model is subsequently used to accurately determine if a street has been assigned a correct/incorrect semantic class.

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Multi-granular Street Network Representation towards Quality Assessment of OpenStreetMap Data

Cited by 21 publications

References 12 publications

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data

Using Latent Semantic Analysis to Identify Research Trends in OpenStreetMap

Automated highway tag assessment of OpenStreetMap road networks

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