OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

John E. Vargas Munoz*, Shivangi Srivastava, Devis Tuia, Alexandre X. Falcao

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

33 Citations (Scopus)


OpenStreetMap (OSM) is a community-based, freely available, editable map service created as an alternative to authoritative sources. 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 article, we review recent methods based on machine learning to improve and use OSM data. Such methods aim to either 1) improve the coverage and quality of OSM layers, typically by using geographic information systems (GISs) and remote sensing technologies, or 2) use the existing OSM layers to train models based on image data to serve applications such as navigation and 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 mapmaking and its quality to the level needed for global and up-to-date land mapping. A preliminary version of this manuscript was presented in [120].
Original languageEnglish
Pages (from-to)184-199
JournalIEEE Geoscience and Remote Sensing Magazine
Issue number1
Early online date17 Jun 2020
Publication statusPublished - Mar 2021


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