从原始轨迹数据中检测停留点（staypoint）是众多空间计算应用的基础任务。该过程将原始的地理坐标数值序列转化为具有语义意义的位置，例如住所、工作场所或餐厅。尽管停留点检测对语义化轨迹分析至关重要，但该任务尚缺乏标准基准，且现有算法从未被系统性地评估过。这一空白持续存在的原因在于：目前尚无公开可用的数据集同时提供个体原始轨迹及其对应的停留点真值标注。本基准论文通过两项关键贡献弥补该局限：（1）我们构建了16个大规模模拟数据集，涵盖数千名智能体在不同轨迹噪声水平下的带标注停留点；（2）我们评估了九种停留点检测算法——包括当前最先进的方法及若干新提出的方法——以分析其对噪声的鲁棒性。评估结果表明，现有最先进算法在真实噪声条件下表现较差；相比之下，我们提出的无监督方法实现了显著改进，而有监督方法则大幅超越现有基线。尽管这些结果极具前景，但本文所提供的数据集与方法仅旨在为未来停留点检测研究提供起点。

Detecting staypoints from raw trajectory data is fundamental to numerous spatial computing applications. This process transforms raw numeric sequences of geolocations into semantically meaningful locations, such as homes, workplaces, or restaurants. Despite its importance for semantic trajectory analysis, staypoint detection lacks standard benchmarks, and existing algorithms have never been systematically evaluated. This gap persists because no publicly available datasets provide both raw individual trajectories and ground-truth staypoint annotations. This benchmark paper addresses this limitation with two key contributions: (1) we introduce 16 large-scale simulated datasets capturing thousands of agents with annotated staypoints across varying trajectory noise levels, and (2) we evaluate nine staypoint detection algorithms-including both state-of-the-art and novel methods-to analyze their robustness to noise. Our evaluation reveals that existing state-of-the-art algorithms perform poorly under realistic noise conditions. Conversely, our proposed unsupervised methods yield substantial improvements, while supervised approaches drastically outperform existing baselines. While these results are very promising, these datasets and methods are only meant as starting points for future research in staypoint detection.