Integrating time-synchronized video with other geospatial and temporal data for remote science operations

Exploration Ground Data Systems (xGDS) supports rapid scientific decision making by synchronizing video in context with map, instrument data visualization, geo-located notes and any other collected data. xGDS is an open source web-based software suite developed at NASA Ames Research Center to support remote science operations in analog missions and prototype solutions for remote planetary exploration. (See Appendix B) Typical video systems are designed to play or stream video only, independent of other data collected in the context of the video. Providing customizable displays for monitoring live video and data as well as replaying recorded video and data helps end users build up a rich situational awareness. xGDS was designed to support remote field exploration with unreliable networks. Commercial digital recording systems operate under the assumption that there is a stable and reliable network between the source of the video and the recording system. In many field deployments and space exploration scenarios, this is not the case — there are both anticipated and unexpected network losses. xGDS' Video Module handles these interruptions, storing the available video, organizing and characterizing the dropouts, and presenting the video for streaming or replay to the end user including visualization of the dropouts. Scientific instruments often require custom or expensive software to analyze and visualize collected data. This limits the speed at which the data can be visualized and limits access to the data to those users with the software. xGDS' Instrument Module integrates with instruments that collect and broadcast data in a single snapshot or that continually collect and broadcast a stream of data. While seeing a visualization of collected instrument data is informative, showing the context for the collected data, other data collected nearby along with events indicating current status helps remote science teams build a better understanding of the environment. Further, sharing geo-located, tagged notes recorded by the scientists and others on the team spurs deeper analysis of the data.