触觉步行表面指示器（TWSI）是盲人与低视力（BLV）行人用于定位人行横道及危险区域的关键安全地标。通过与BLV导盲犬使用者、训犬师及定向行走（O&M）专家的实地观察交流，我们确认了高可靠性与高精度的TWSI分割对BLV人群导航辅助至关重要。实现该可靠性需依赖大规模标注数据。然而，现有城市感知数据集中TWSI样本严重不足；即便是已有的专用铺装数据集也存在局限：其缺乏机器人适用视角（如第一人称视角或俯视视角），且地理分布偏向东亚地区常用的导向条（即沿人行道连续铺设的凸起平行条状结构）。这种狭隘聚焦忽略了截顶圆锥体（即北美与欧洲普遍采用的圆形凸起阵列），后者主要用于路缘、人行横道及站台边缘等位置，作为可探测的警示标识。因此，仅基于导向条数据训练的模型难以泛化至圆锥体警示标识，导致在关键安全场景中出现漏检或误停。

Tactile Walking Surface Indicators (TWSIs) are safety-critical landmarks that blind and low-vision (BLV) pedestrians use to locate crossings and hazard zones. From our observation sessions with BLV guide dog handlers, trainers, and an O&M specialist, we confirmed the critical importance of reliable and accurate TWSI segmentation for navigation assistance of BLV individuals. Achieving such reliability requires large-scale annotated data. However, TWSIs are severely underrepresented in existing urban perception datasets, and even existing dedicated paving datasets are limited: they lack robot-relevant viewpoints (e.g., egocentric or top-down) and are geographically biased toward East Asian directional bars - raised parallel strips used for continuous guidance along sidewalks. This narrow focus overlooks truncated domes - rows of round bumps used primarily in North America and Europe as detectable warnings at curbs, crossings, and platform edges. As a result, models trained only on bar-centric data struggle to generalize to dome-based warnings, leading to missed detections and false stops in safety-critical environments.