OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

TL;DR

This paper reviews recent machine learning methods applied to OpenStreetMap data, highlighting challenges in data quality and exploring opportunities for improved land mapping and remote sensing applications.

Contribution

It provides a comprehensive overview of how machine learning enhances OSM data quality and utility in geosciences and remote sensing.

Findings

Machine learning can improve OSM coverage and accuracy.

ML models enable land use classification using OSM and remote sensing data.

OpenStreetMap data, combined with ML, can transform land mapping practices.

Abstract

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.