OpenStreetMap: Challenges and Opportunities in Machine Learning and Remote Sensing

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.

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Cited by in corpus (10)

• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images
• Towards a Collective Agenda on AI for Earth Science Data Analysis
• ObjFormer: Learning Land-Cover Changes From Paired OSM Data and Optical High-Resolution Imagery via Object-Guided Transformer
• Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
• Better, Not Just More: Data-Centric Machine Learning for Earth Observation
• Attention-Based Vandalism Detection in OpenStreetMap
• LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives
• Transport-Related Surface Detection with Machine Learning: Analyzing Temporal Trends in Madrid and Vienna
• Bidirectional yet asymmetric causality between urban systems and traffic dynamics in 30 cities worldwide
• Flexible Keyword-Aware Top-$k$ Route Search