@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/entities/document/10.4230/LIPIcs.GIScience.2023.53},
  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}
}