A robust indoor/outdoor detection method based on spatial and temporal features of sparse GPS measured positions

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.