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. In this paper, we formulate the T2O global localization task, which aims to estimate accurate 2 degree-of-freedom (DoF) positions in urban environments from textual scene descriptions without relying on geometric observations or GNSS-based initial location. To support the proposed task, we introduce TOL, a large-scale benchmark spanning multiple continents and diverse urban environments. TOL contains approximately 121K textual queries paired with OSM map tiles and covers about 316 km of road trajectories across Boston, Karlsruhe, and Singapore. We further propose TOLoc, a coarse-to-fine localization framework that explicitly models the semantics of surrounding objects and their directional information. In the coarse stage, direction-aware features are extracted from both textual descriptions and OSM tiles to construct global descriptors, which are used to retrieve candidate locations for the query. In the fine stage, the query text and top-1 retrieved tile are jointly processed, where a dedicated alignment module fuses textual descriptor and local map features to regress the 2-DoF pose. Experimental results demonstrate that TOLoc achieves strong localization performance, outperforming the best existing method by 6.53%, 9.93%, and 8.31% at 5m, 10m, and 25m thresholds, respectively, and shows strong generalization to unseen environments. Dataset, code and models will be publicly available at: https://github.com/WHU-USI3DV/TOL.

翻译：自然语言为地理空间应用中表达空间意图提供了一种直观方式。现有定位方法通常依赖密集点云地图或高分辨率影像，而OpenStreetMap（OSM）提供了一种紧凑且自由可用的地图表示，编码了丰富的语义和结构信息，适合大规模定位。然而，文本到OSM（T2O）定位仍鲜有探索。本文中，我们提出了T2O全局定位任务，旨在通过文本场景描述，在不依赖几何观测或基于GNSS的初始位置的情况下，在城市环境中估计精确的2自由度（DoF）位置。为支持所提任务，我们介绍了TOL，一个覆盖多个大洲及多样城市环境的大规模基准。TOL包含约121K条文本查询，对应OSM地图瓦片，覆盖波士顿、卡尔斯鲁厄和新加坡约316公里的道路轨迹。我们进一步提出了TOLoc，一个由粗到细的定位框架，显式建模了周围物体的语义及其方向信息。在粗阶段，从文本描述和OSM瓦片中提取方向感知特征，构建全局描述子以检索查询的候选位置。在细阶段，查询文本与排名第一的检索瓦片联合处理，通过专用对齐模块融合文本描述子和局部地图特征，以回归2-DoF位姿。实验结果表明，TOLoc实现了强定位性能，在5m、10m和25m阈值下分别比现有最佳方法高出6.53%、9.93%和8.31%，并对未见环境表现出强泛化能力。数据集、代码和模型将公开发布于：https://github.com/WHU-USI3DV/TOL。