In this study, the smartphone global navigation satellite system (GNSS) positioning accuracy was improved by selecting optimal visible satellites through a 3D surface model and the shadow matching (SM) technique. A 3D surface model was constructed using an unmanned aerial vehicle (UAV) to obtain an accurate terrain model and perform visibility analysis. Additionally, we used the geographic information system (GIS) analysis as well as the skyline and barrier analysis methods to calculate the visibility between smartphones and satellites. The altitudes of the satellites were calculated to analyze the visibility between the analyzed smartphone and the satellites, and the visible satellites were selected by a sky mask method. Visible satellites were classified through the analysis of the signal characteristics by investigating the observed elevation angle of the satellite signal, the carrier-to-noise ratio (C/No), and the pseudorange ratio consistency (Prc). Moreover, the satellites were categorized via two classification methods and then recombined by statistical analysis to optimally select the visible satellites. Furthermore, the smartphone’s location was computed using the optimal combination of satellites, and the accuracy was evaluated by comparing the calculated location coordinates with the true position coordinates. As a result, the maximum rates of improvement were 880, 356, and 5% in environments of low-rise building urban, high-rise building urban, and surrounded by tall buildings, respectively.