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.

翻译：触觉行走表面指示器（TWSIs）是视障与低视力（BLV）行人用于定位人行横道与危险区域的关键安全地标。通过与BLV导盲犬使用者、训练师及定向行走专家的观察交流，我们确认了可靠且精确的TWSI分割对于BLV个体导航辅助的至关重要性。实现此类可靠性需要大规模标注数据。然而，现有城市感知数据集中TWSI的表征严重不足，即使现有专用铺面数据集也存在局限：它们缺乏机器人相关视角（如第一人称或俯视视角），且在地域分布上过度偏重东亚地区的导向条——用于沿人行道连续引导的凸起平行条纹。这种狭隘的聚焦忽略了截顶圆点砖——主要应用于北美和欧洲路缘、人行横道及站台边缘作为可探测警示标志的成排圆形凸起。因此，仅基于导向条数据训练的模型难以泛化至圆点式警示砖，导致在关键安全环境中出现漏检与误停。