Staypoint Detection from Noisy Trajectory Data [Experiment Paper]
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 limita
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- FuzzyOverlapping authors or contributors · 62%modular/modular →
“Shared author/contributor keys: liu”
- FuzzyOverlapping authors or contributors · 62%Kong/kong →
“Shared author/contributor keys: kong”
- LinkedLinked via arxiv author · 85%Lance Kennedy →
“Staypoint Detection from Noisy Trajectory Data [Experiment Paper]”
- LinkedLinked via arxiv author · 85%Hossein Amiri →
“Staypoint Detection from Noisy Trajectory Data [Experiment Paper]”
- LinkedLinked via arxiv author · 85%Yueyang Liu →
“Staypoint Detection from Noisy Trajectory Data [Experiment Paper]”
- LinkedLinked via arxiv author · 85%Riyang Bao →
“Staypoint Detection from Noisy Trajectory Data [Experiment Paper]”
- LinkedLinked via arxiv author · 85%Hanqi Chen →
“Staypoint Detection from Noisy Trajectory Data [Experiment Paper]”
- LinkedLinked via arxiv author · 85%Mohammad Hashemi →
“Staypoint Detection from Noisy Trajectory Data [Experiment Paper]”