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Automating the Analysis of Spatial Grids

A Practical Guide to Data Mining Geospatial Images for Human & Environmental Applications

  • Book
  • © 2012

Overview

Authors:
  1. Valliappa Lakshmanan

    1. , National Weather Center, University of Oklahoma, Norman, USA

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  • Contains a distillation of practical techniques from multiple fields
  • Features techniques illustrated on an easy to understand (and included) population density data set
  • Includes software implementation in Java of the techniques described in the text

Part of the book series: Geotechnologies and the Environment (GEOTECH, volume 6)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-x
    Download chapter PDF

  2. Automated Analysis of Spatial Grids: Motivation and Challenges

    • Valliappa Lakshmanan
    Pages 1-18

  3. Geospatial Grids

    • Valliappa Lakshmanan
    Pages 19-66

  4. Data Structures for Spatial Grids

    • Valliappa Lakshmanan
    Pages 67-89

  5. Global and Local Image Statistics

    • Valliappa Lakshmanan
    Pages 91-128

  6. Neighborhood and Window Operations

    • Valliappa Lakshmanan
    Pages 129-172

  7. Identifying Objects

    • Valliappa Lakshmanan
    Pages 173-221

  8. Change and Motion Estimation

    • Valliappa Lakshmanan
    Pages 223-269

  9. Data Mining Attributes from Spatial Grids

    • Valliappa Lakshmanan
    Pages 271-308

  10. Back Matter

    Pages 309-320
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Keywords

About this book

The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets. The aim is for readers to be able to devise and implement automated techniques to extract information from spatial grids such as radar, satellite or high-resolution survey imagery.

Authors and Affiliations

  • , National Weather Center, University of Oklahoma, Norman, USA

    Valliappa Lakshmanan

About the author

Dr. Valliappa Lakshmanan is an expert in machine intelligence R&D for meteorological applications, and in designing and developing large-scale software systems. He is skilled in communicating technical and non-technical material to diverse audiences. He has studied at the Indian Institute of Technology in Madras, the Ohio State University in Columbus and the University of Oklahoma.
Dr. Lakshmanan is currently employed as a Research Scientist at CIMMS, being the technical lead on several software projects and research groups.
He also develops automated real-time pattern recognition algorithms and visualization techniques for weather phenomena.
He has (co-)written many journal articles.

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