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The number of satellites visible to a Global Navigation Satellite System (GNSS)\udreceiver is important for high accuracy surveys. To aid with this, there are software packages\udcapable of predicting GNSS visibility at any location of the globe at any time of day. These\udprediction packages operate by using regularly updated almanacs containing positional data\udfor all navigation satellites; however, one issue that restricts their use is that most packages\udassume that there are no obstructions on the horizon. In an attempt to improve this, certain\udplanning packages are now capable of modelling simple obstructions whereby portions of the\udhorizon visible from one location can be blocked out, thereby simulating buildings or other\udvertical structures. While this is useful for static surveys, it is not applicable for dynamic\udsurveys when the GNSS receiver is in motion. This problem has been tackled in the past by\udusing detailed, high-accuracy building models and designing novel methods for modelling\udsatellite positions using GNSS almanacs, which is a time-consuming and costly approach.\udThe solution proposed in this paper is to use a GIS to combine existing, freely available\udGNSS prediction software to predict pseudo satellite locations, incorporate a 2.5D model\udof the buildings in an area created with national mapping agency 2D vector mapping and\udlow density elevation data to minimise the need for a full survey, thereby providing savings\udin terms of cost and time. Following this, the ESRI ArcMap viewshed tool was used to\udascertain what areas exhibit poor GNSS visibility due to obstructions over a wide area, and\udan accuracy assessment of the procedure was made

Combining 2D Mapping and Low Density Elevation Data in a GIS for GNSS Shadow Prediction