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Remote Sensing Big Data - Eugene Yu - Bog

Bag om Remote Sensing Big Data

This monograph provides comprehensive coverage of the collection, management, and use of big data obtained from remote sensing. The book begins with an introduction to the basics of big data and remote sensing, laying the groundwork for the more specialized information to follow. The volume then goes on to address a wide variety of topics related to the use and management of remote sensing big data, including hot topics such as analysis through machine learning, cyberinfrastructure, and modeling. Examples on how to use the results of big data analysis of remotely sensed data for concrete decision-making are offered as well. The closing chapters discuss geospatial big data initiatives throughout the world and future challenges and opportunities for remote sensing big data applications. The audience for this book includes researchers at the intersection of geoscience and data science, senior undergraduate and graduate students, and anyone else interested in how large datasets obtained through remote sensing can be best utilized. The book presents a culmination of 30 years of research from renowned spatial scientists Drs. Liping Di and Eugene Yu.

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  • Sprog:
  • Engelsk
  • ISBN:
  • 9783031339318
  • Indbinding:
  • Hardback
  • Sideantal:
  • 308
  • Udgivet:
  • 25. juli 2023
  • Udgave:
  • 23001
  • Størrelse:
  • 160x23x241 mm.
  • Vægt:
  • 629 g.
  • 8-11 hverdage.
  • 5. december 2024
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  • BLACK NOVEMBER

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Prøv i 30 dage for 45 kr.
Herefter fra 79 kr./md. Ingen binding.

Beskrivelse af Remote Sensing Big Data

This monograph provides comprehensive coverage of the collection, management, and use of big data obtained from remote sensing. The book begins with an introduction to the basics of big data and remote sensing, laying the groundwork for the more specialized information to follow. The volume then goes on to address a wide variety of topics related to the use and management of remote sensing big data, including hot topics such as analysis through machine learning, cyberinfrastructure, and modeling. Examples on how to use the results of big data analysis of remotely sensed data for concrete decision-making are offered as well. The closing chapters discuss geospatial big data initiatives throughout the world and future challenges and opportunities for remote sensing big data applications.
The audience for this book includes researchers at the intersection of geoscience and data science, senior undergraduate and graduate students, and anyone else interested in how large datasets obtained through remote sensing can be best utilized. The book presents a culmination of 30 years of research from renowned spatial scientists Drs. Liping Di and Eugene Yu.

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