Learning to Improve Earth Observation Flight Planning

Description

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.

Resources

Name Format Description Link
33 mooz08b.pdf https://c3.nasa.gov/dashlink/static/media/publication/mooz08b.pdf
34 2A54_BR.080202.12.KWAJ.7.PNG https://disc2.gesdisc.eosdis.nasa.gov/data/TRMM_GV_L2/TRMM_2A54.7/2008/033/2A54_BR.080202.12.KWAJ.7.PNG
21 Access the dataset landing page from the GES DISC website. https://disc.gsfc.nasa.gov/datacollection/TRMM_2A54UW_7.html
21 Access the data via HTTPS https://disc2.gesdisc.eosdis.nasa.gov/data/TRMM_GV_L2/TRMM_2A54UW.7
21 Use the Earthdata Search to find and retrieve data sets across multiple data centers. https://search.earthdata.nasa.gov/search?q=TRMM_2A54UW
21 PMM Project Home Page https://pmm.nasa.gov
21 File specification document https://atmos.washington.edu/gcg/MG/KWAJ/UWproductsOnDAAC.html
21 TRMM Field Campaign Project Page https://atmos.washington.edu/gcg/MG/KWAJ/GV.html
21 TRMM Data Gaps https://gpmweb2https.pps.eosdis.nasa.gov/tsdis/AB/docs/anomalous.html

Tags

  • dashlink
  • nasa
  • ames

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