Automated highway tag assessment of OpenStreetMap road networks

Jilani, Musfira; Corcoran, Padraig; Bertolotto, Michela

doi:10.1145/2666310.2666476

Cited by 41 publications

(30 citation statements)

References 8 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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“…Ali and Schmid () designed a classifier that learns the correct class of existing entities (i.e., parks and gardens) on the basis of their characteristics (e.g., size) and used it to predict the correct class of a new entity. 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%

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

Degrossi

Albuquerque

Rocha

et al. 2018

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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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“…This structure combines the primal (where nodes are road intersections) and dual representations (where nodes are fragments of roads) of road networks. This multigranular representation is used in [6] to extract features and train a Random Forest classifier that is able to classify streets to 21 different street categories in OSM. The method uses Bag of Words computed over geometrical and topological features of the analyzed streets and their neighbors.…”

Section: Extracting Roads From Aerial Imagesmentioning

confidence: 99%

“…In [72], the authors propose a CNN-based method to detect buildings in aerial imagery using an iterative process, in which new samples are selected for annotation by an active learning method and the model is retrained. In [73] the authors use multiple sources of crowdsourced geographical data (namely OSM, MapSwipe 6 , and OsmAnd 7 ) and an active learning strategy to train a CNN model that detects image patches with buildings. Furthermore, the authors perform an experiment in MapSwipe (smartphone-based application for humanitarian mapping) asking the volunteers to just verify the tiles that are selected by a trained classifier, saving considerably the user effort and obtaining an accurate classifier.…”

Section: B Building Detection and Segmentationmentioning

confidence: 99%

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

Muñoz

Srivastava

Tuia

et al. 2021

IEEE Geosci. Remote Sens. Mag.

102

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OpenStreetMap (OSM) is a community-based, freely available, editable map service that was created as an alternative to authoritative ones. Given that it is edited mainly by volunteers with different mapping skills, the completeness and quality of its annotations are heterogeneous across different geographical locations. Despite that, OSM has been widely used in several applications in Geosciences, Earth Observation and environmental sciences. In this work, we present a review of recent methods based on machine learning to improve and use OSM data. Such methods aim either 1) at improving the coverage and quality of OSM layers, typically using GIS and remote sensing technologies, or 2) at using the existing OSM layers to train models based on image data to serve applications like navigation or land use classification. We believe that OSM (as well as other sources of open land maps) can change the way we interpret remote sensing data and that the synergy with machine learning can scale participatory map making and its quality to the level needed to serve global and up-to-date land mapping. A preliminary version of this manuscript has been presented in the first authors dissertation [1].

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Automated highway tag assessment of OpenStreetMap road networks

Cited by 41 publications

References 8 publications

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data

Extending Processing Toolbox for assessing the logical consistency of OpenStreetMap data

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

OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

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