GNSS navigation can be challenging in urban environments, especially when low-cost devices are adopted. Among the possible solutions, in more recent years, approaches based on Machine Learning became popular. In this work features based on geometry, satellite visibility and carrier-to-noise ratio are used in combination with k-Nearest Neighbors (kNN) classifier to distinguish between open-sky and obstructed environments. The purpose of this research is to develop a reliable context classifier, to evaluate its recognition capabilities in static and dynamic environments and to assess its applicability in real-time positioning. Several performance metrics have been used, i.e., accuracy, precision, recall, F1-score, and multiple tests have been carried out to demonstrate the reliability of such algorithm with validation data. More than 98% of classification accuracy for the static tests has been obtained in average, evidencing the detection capabilities of such an algorithm.