AI Assisted Trail Map Generation based on Public GPS Data

Abstract

Hiking trail maps are typically created manually by survey, a time-consuming process. This process is expensive and must be repeated over time to improve accuracy. This paper proposed an inexpensive, automatic, and accurate trail network generation method from anonymous public GPS data utilizing a growing self-organizing map (GSOM). This technique does not rely on sequential GPS traces to learn network topology, unlike other approaches. Tuning several hyper-parameters can adjust this process for datasets and networks with unique characteristics. Reconstruction and adaption are also possible based on newly acquired data sources. Constructed trail maps, trained on publicly available GPS data, are compared against a ground truth map from Open Street Map (OSM). Performance is evaluated based on completeness, accuracy, and topological correctness. Testing on sparse networks with minimal GPS noise suggests favorable performance.