AI RESEARCH
TOL: Textual Localization with OpenStreetMap
arXiv CS.CV
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ArXi:2604.01644v1 Announce Type: new Natural language provides an intuitive way to express spatial intent in geospatial applications. While existing localization methods often rely on dense point cloud maps or high-resolution imagery, OpenStreetMap (OSM) offers a compact and freely available map representation that encodes rich semantic and structural information, making it well suited for large-scale localization. However, text-to-OSM (T2O) localization remains largely unexplored.