A Robust Indoor/Outdoor Detection Method Based on Spatial and Temporal Features of Sparse GPS Measured Positions


IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E102-A   No.6   pp.860-865
Publication Date: 2019/06/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E102.A.860
Type of Manuscript: LETTER
Category: Intelligent Transport System
indoor/outdoor detection,  sparse GPS,  random forest classifier,  

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Cell phones with GPS function as well as GPS loggers are widely used and we can easily obtain users' geographic information. Now classifying the measured GPS positions into indoor/outdoor positions is one of the major challenges. In this letter, we propose a robust indoor/outdoor detection method based on sparse GPS measured positions utilizing machine learning. Given a set of clusters of measured positions whose center position shows the user's estimated stayed position, we calculate the feature values composed of: positioning accuracy, spatial features, and temporal feature of measured positions included in every cluster. Then a random forest classifier learns these feature values of the known data set. Finally, we classify the unknown clusters of measured positions into indoor/outdoor clusters using the learned random forest classifier. The experiments demonstrate that our proposed method realizes the maximum F1 measure of 1.000, which classifies measured positions into indoor/outdoor ones with almost no errors.