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    Turning Night into Day: The Creation and Validation of Synthetic Nighttime Visible Imagery Using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) and Machine Learning

    Source: Artificial Intelligence for the Earth Systems:;2024:;volume( 003 ):;issue: 003
    Author:
    Pasillas, Chandra M.
    ,
    Kummerow, Christian
    ,
    Bell, Michael
    ,
    Miller, Steven D.
    DOI: 10.1175/AIES-D-23-0002.1
    Publisher: American Meteorological Society
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      Turning Night into Day: The Creation and Validation of Synthetic Nighttime Visible Imagery Using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) and Machine Learning

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4299545
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    contributor authorPasillas, Chandra M.
    contributor authorKummerow, Christian
    contributor authorBell, Michael
    contributor authorMiller, Steven D.
    date accessioned2024-12-24T13:45:04Z
    date available2024-12-24T13:45:04Z
    date copyright01 Jul. 2024
    date issued2024
    identifier otheraies-AIES-D-23-0002.1.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4299545
    languageEnglish
    publisherAmerican Meteorological Society
    titleTurning Night into Day: The Creation and Validation of Synthetic Nighttime Visible Imagery Using the Visible Infrared Imaging Radiometer Suite (VIIRS) Day–Night Band (DNB) and Machine Learning
    typeJournal Paper
    journal volume3
    journal issue3
    journal titleArtificial Intelligence for the Earth Systems
    identifier doi10.1175/AIES-D-23-0002.1
    journal lastpagee230002
    treeArtificial Intelligence for the Earth Systems:;2024:;volume( 003 ):;issue: 003
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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