# Staypoint Detection from Noisy Trajectory Data [Experiment Paper]

arXiv: https://arxiv.org/abs/2607.19312 · HTML: https://mlsanity.com/paper/2607.19312/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 21 Jul 2026.

## Builds on (most cited first, 5 of 5)

- [Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization](https://mlsanity.com/paper/1603.06560/) (2016)
- [Large Language Models for Spatial Trajectory Patterns Mining](https://mlsanity.com/paper/2310.04942/) (2023)
- [The Patterns of Life Human Mobility Simulation](https://mlsanity.com/paper/2410.00185/) (2024)
- [Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories](https://mlsanity.com/paper/2410.00054/) (2024)
- [Neural Collaborative Filtering to Detect Anomalies in Human Semantic Trajectories](https://mlsanity.com/paper/2409.18427/) (2024)

## Abstract (arXiv)

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.