Dataset Identification:
Resource 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.
Citation
- Title Machine Learning for Earth Observation Flight Planning Optimization
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- revision Date
2012-02-26T20:33:00
Resource language:
[u'en-US']
Constraints on resource usage:
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- Constraints
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- Use limitation statement:
- public
point of contact
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publisher
- individual Name {u'hasEmail': u'mailto:Nikunj.C.Oza@nasa.gov', u'fn': u'Nikunj Oza'}
- organisation Name
{u'subOrganizationOf': {u'subOrganizationOf': {u'name': u'U.S. Government'}, u'name': u'National Aeronautics and Space Administration'},
u'name': u'Dashlink'}
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- Contact information
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- Address
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- electronic Mail Address
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Metadata data stamp:
2012-02-26T20:33:00
Metadata contact
-
publisher
Metadata scope code
dataset
Metadata standard for this record:
ISO 19115:2003 - Geographic information - Metadata
standard version:
ISO 19115:2003
Metadata record identifier:
DASHLINK_543
Metadata record format is ISO19139 XML (MD_Metadata)