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

Authors Salim Miloudi , Bouhadjar Meguenni

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Salim Miloudi
  • Spatial Reference Information Systems Department, Space Techniques Center, Oran, Algeria
Bouhadjar Meguenni
  • Spatial Reference Information Systems Department, Space Techniques Center, Oran, Algeria

Acknowledgements

We would like to thank the anonymous reviewers for their valuable comments.

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Salim Miloudi and Bouhadjar Meguenni. Exploring the Potential of Machine and Deep Learning Models for OpenStreetMap Data Quality Assessment and Improvement (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 53:1-53:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.53

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.

Subject Classification

ACM Subject Classification
  • Information systems → Geographic information systems
Keywords
  • OpenStreetMap (OSM)
  • Volunteered Geographic Information (VGI)
  • Machine Learning (ML)
  • Deep Learning (DL)
  • Quality Assessment (QA)
  • Building Footprint Detection
  • Semantic Segmentation

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