Learning to improve earth observation flight planning

Authors:
Robert A. Morris;Nikunj Oza;Leslie Keely;Elif Kürklü;Anthony Strawa
Affiliations:
Intelligent Systems Division, NASA Ames Research Center;Intelligent Systems Division, NASA Ames Research Center;Intelligent Systems Division, NASA Ames Research Center;Perot Systems Government Services, NASA Ames Research Center and Intelligent Systems Division, NASA Ames Research Center;Earth Sciences Division, NASA Ames Research Center
Venue:
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Year:
2008

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Abstract

This paper describes a method and system for integrating machine learning with planning and data visualization for the management of mobile sensors for Earth science investigations. Data mining identifies discrepancies between previous observations and predictions made by Earth science models. Locations of these discrepancies become interesting targets for future observations. Such targets become goals used by a flight planner to generate the observation activities. The cycle of observation, data analysis and planning is repeated continuously throughout a multi-week Earth science investigation.