Abstract: A machine learning model classifies points of interest in a parallel reality game hosted by a server. The server generates training data sets that include verified properties for points of interest. The machine learning model may predict unverified properties for points of interest. Players in the parallel reality game may input properties for the points of interest. The machine learning model use the received properties from players as inputs to the machine learning model to verify unverified properties or generate new properties for the points of interest. The server may classify the points of interest as suitable for particular activities, and the server may use the classifications for future activities within the parallel reality game.

Abstract: Systems and methods for linking real world activity with a location-based parallel reality experience are provided. In particular, a game server hosting a parallel reality experience receives position data from a client device of a user and determines whether the client device is at a real world landmark associated with a geographic area. Responsive to determining that the client device is at the real world landmark, the game server cause the client device to present content incentivizing the user to travel to a real world location in the geographic area. The game server determines whether the client device is within proximity of the real world location based on sensor data. Responsive to determining that the client device is within proximity of the real world location, the game server causes the client device to display the virtual element at a virtual world location corresponding to the real world location.