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Environmental Modeling Using Satellite Imaging and Dataset Re-processing

  • Book
  • © 2019

Overview

  • I?ntroduces methods of re-processing images to extract numerical information
  • Helps to solve the challenges of discarding datasets that are relevant directly or indirectly to the research
  • Enlightens researchers on how to analyse measurements with the aim of ensuring 100% utilization

Part of the book series: Studies in Big Data (SBD, volume 54)

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Table of contents (6 chapters)

Keywords

About this book

This book introduces methods of re-processing images to extract numerical information that can be used to quantify the observables in environmental modelling. Experiments or procedures that yield large images can be statistically or parametrically examined. Through the use of open source libraries, the book shows how ‘big data’ in the form of images or datasets can be comparatively analysed along same defined procedures or standards.  This book helps to solve the challenges of discarding datasets that are relevant directly or indirectly to the research. The habit of screening datasets leads to the discard of over 90% of the original dataset or images generated in the experiments or procedure. If the images or datasets are generated under the same principles or conditions, then each measurement may be the narrative of unique events. The focus of this book is to enlighten researchers on how to analyse measurements with the aim of ensuring 100% utilization.

Authors and Affiliations

  • Department of Physics, Covenant University, Ota, Nigeria

    Moses Eterigho Emetere

  • Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, South Africa

    Moses Eterigho Emetere

About the author

M.E. Emetere had his doctorate degrees in Solid State Physics (PhD) and Atmospheric Physics (PhD). He is faculty at Covenant University, Nigeria and a postdoctoral fellow at University of Johannesburg. He won the prestigious AU/TWAS Young Scientist National Award (Earth and Life Sciences) in 2015. 

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