Nikunj C. Oza's Publications

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Machine Learning for Earth Observation Flight Planning Optimization

Machine Learning for Earth Observation Flight Planning Optimization. Elif Kurklu, Robert M. Morris, and Nikunj C. Oza. In AAAI Spring Symposium Series, Workshop on Semantic Scientific Knowledge Integration, March 2008.

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Abstract

This paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advancedtechniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. This paper describes the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.

BibTeX Entry

@inproceedings{kumo08,
	author = {Elif Kurklu, Robert M. Morris, and Nikunj C. Oza},
	title = {Machine Learning for Earth Observation Flight Planning Optimization},
	booktitle={AAAI Spring Symposium Series, Workshop on Semantic Scientific Knowledge Integration},
	month={March},
	abstract={This paper is a progress report of an effort whose goal is to demonstrate the effectiveness of automated data mining and planning for the daily management of Earth Science missions. Currently, data mining and machine learning technologies are being used by scientists at research labs for validating Earth science models. However, few if any of these advanced
techniques are currently being integrated into daily mission operations. Consequently, there are significant gaps in the knowledge that can be derived from the models and data that are used each day for guiding mission activities. The result can be sub-optimal observation plans, lack of useful data, and wasteful use of resources. Recent advances in data mining, machine learning, and planning make it feasible to migrate these technologies into the daily mission planning cycle. This paper describes the design of a closed loop system for data acquisition, processing, and flight planning that integrates the results of machine learning into the flight planning process.},
	bib2html_pubtype = {Refereed Conference},
	bib2html_rescat = {Planning and Data Mining},
	year = {2008}
}

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