NCGIA Core Curriculum in Geographic Information Science
URL: "http://www.ncgia.ucsb.edu/giscc/units/u188/u188.html"

Unit 188 - Artificial Neural Networks 
for Spatial Data Analysis

Written by Sucharita Gopal
Department of Geography and Centre for Remote Sensing
Boston University, Boston MA 02215

DRAFT - comments invited

This unit is part of the NCGIA Core Curriculum in Geographic Information Science. These materials may be used for study, research, and education, but please credit the author, Sucharita Gopal, and the project, NCGIA Core Curriculum in GIScience. All commercial rights reserved. Copyright 1998 by Sucharita Gopal.

Your comments on these materials are welcome. A link to an evaluation form is provided at the end of this document.


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After learning the material in this unit, students should be able to:

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Unit 188 - Artificial Neural Networks
for Spatial Data Analysis

1. Introduction

1.1. What are Artificial Neural Networks (ANN)?

1.2. Some Definitions of ANN

1.3. Brief History of ANN

1.4. Applications of ANN

1.5. Differences between ANN and AI approaches:

1.6. ANN in Apatial Analysis and Geography

1.7. Relationship between Statistics and ANN


2. Types of ANN

2.1. Networks based on supervised and unsupervised learning

2.1.1. Supervised Learning

2.1.2. Self-Organization or Unsupervised Learning

2.2. Networks based on Feedback and Feedforward connections


3. Methodology: Training, Testing and Validation Datasets


4. Application of a Supervised ANN for a Classification Problem

4.1.  Multi-Layer Perceptron (MLP) Using Backpropagation

4.1.1. Things to note while using the backpropagation algorithm

4.2. Fuzzy ARTMAP

4.2.1. Basic architecture of fuzzy ARTMAP

4.3. Software


5. Application Exercises:  Backpropagation algorithm X Fuzzy ARTMAP for Classification of Landcover Classes

5.1. Data Set 1

5.2. Data Set 2


6. Summary

  • This unit has introduced some definitions and types of neural networks

  • 7. Review and Study Questions


    8. References

    8.1. References in the text of this unit

    8.2. Books

    8.3. Classics



    Evaluation

    We are very interested in your comments and suggestions for improving this material.  Please follow the link above to the evaluation form if you would like to contribute in this manner to this evolving project.


    Citation

    To reference this material use the appropriate variation of the following format: Sucharita Gopal. (1998) Artificial Neural Networks for Spatial Data Analysis, NCGIA Core Curriculum in GIScience, http://www.ncgia.ucsb.edu/giscc/units/u188/u188.html, posted December 22, 1998.

    The correct URL for this page is: http://www.ncgia.ucsb.edu/giscc/units/u188/u188.html
    Created: November 23, 1998.  Last revised: December 22, 1998.


    To the Core Curriculum Outline