A Radio Environment Map (REM) is a powerful tool in enhancing the experience of radio-enabled agents but building such a REM can be a laborious undertaking, especially in three dimensions. This project shows how such a REM of an indoor three-dimensional space can be generated in an autonomous and scalable way. Building on the results of the preceding Research Project 1, multiple drones are used to map the WiFi signals present in such a space in a real-world environment where the drones are each able to visit 36 waypoints and collectively gather thousands of WiFi beacon data samples. This report also includes an analysis of the collected data and concludes by proposing machine-learning based techniques to predict the signal strength of known access points in locations not visited by the drones.