No Perfect Outdoors: Towards A Deep Profiling of GNSS-based Location Contexts

TL;DR

This paper explores deep learning techniques to analyze GNSS data for detailed urban environment profiling, improving indoor-outdoor detection and GPS error estimation beyond existing binary methods.

Contribution

It introduces a deep profiling approach that captures diversified urban contexts from GNSS data, enabling finer semantic classification and better GPS error indicators.

Findings

Enhanced semantic classification surpassing binary indoor-outdoor detection

More meaningful GPS error indicators derived from deep profiling

Validated through extensive data collection and evaluations

Abstract

While both outdoor and indoor localization methods are flourishing, how to properly marry them to offer pervasive localizability in urban areas remains open. Recently proposals on indoor-outdoor detection make the first step towards such an integration, yet complicated urban environments render such a binary classification incompetent. In this paper, we intend to fully explore raw GNSS measurements in order to better characterize the diversified urban environments. Essentially, we tackle the challenges introduced by the complex GNSS data and apply a deep learning model to identify representations for respective location contexts. We further develop two preliminary applications of our deep profiling. On one hand, we offer a more fine-grained semantic classification than binary indoor-outdoor detection. On the other hand, we derive a GPS error indicator more meaningful than that provided by Google Maps. These results are all corroborated by our extensive data collection and trace-driven evaluations.