License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
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DOI: 10.4230/LIPIcs.GIScience.2023.53
URN: urn:nbn:de:0030-drops-189486
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18948/
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Miloudi, Salim ; Meguenni, Bouhadjar

Exploring the Potential of Machine and Deep Learning Models for OpenStreetMap Data Quality Assessment and Improvement (Short Paper)

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LIPIcs-GIScience-2023-53.pdf (0.5 MB)

Abstract

The OpenStreetMap (OSM) project is a widely-used crowdsourced geographic data platform that allows users to contribute, edit, and access geographic information. However, the quality of the data in OSM is often uncertain, and assessing the quality of OSM data is crucial for ensuring its reliability and usability. Recently, the use of machine and deep learning models has shown to be promising in assessing and improving the quality of OSM data. In this paper, we explore the current state-of-the-art machine learning models for OSM data quality assessment and improvement as an attempt to discuss and classify the underlying methods into different categories depending on (1) the associated learning paradigm (supervised or unsupervised learning-based methods), (2) the usage of extrinsic or intrinsic-based metrics (i.e., assessing OSM data by comparing it against authoritative external datasets or via computing some internal quality indicators), and (3) the use of traditional or deep learning-based models for predicting and evaluating OSM features. We then identify the main trends and challenges in this field and provide recommendations for future research aiming at improving the quality of OSM data in terms of completeness, accuracy, and consistency.

BibTeX - Entry

@InProceedings{miloudi_et_al:LIPIcs.GIScience.2023.53,
  author =	{Miloudi, Salim and Meguenni, Bouhadjar},
  title =	{{Exploring the Potential of Machine and Deep Learning Models for OpenStreetMap Data Quality Assessment and Improvement}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{53:1--53:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18948},
  URN =		{urn:nbn:de:0030-drops-189486},
  doi =		{10.4230/LIPIcs.GIScience.2023.53},
  annote =	{Keywords: OpenStreetMap (OSM), Volunteered Geographic Information (VGI), Machine Learning (ML), Deep Learning (DL), Quality Assessment (QA), Building Footprint Detection, Semantic Segmentation}
}

Keywords: OpenStreetMap (OSM), Volunteered Geographic Information (VGI), Machine Learning (ML), Deep Learning (DL), Quality Assessment (QA), Building Footprint Detection, Semantic Segmentation
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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