Discovering geo-spatial networks from ambiguous track data

Wide area motion imagery (WAMI) sensors increasingly are being used for persistent surveillance of large urban areas. One of the potential uses for such surveillance is the discovery of geo-spatial networks, which are sets of locations linked by repeated traffic flow over an extended period of time. In this work we present a simple method of deriving geo-spatial network links automatically from ambiguous track segments or tracklets. The method avoids making explicit tracklet linking decisions and relies on temporal aggregation to identify the persistent origin-destination location pairs. We present experimental network discovery results using simulated high density track data for a downtown urban setting.