  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.
