Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python

4 (1)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every field you can think of from medicine, to defense, to farming. It is an approach to use statistical analysis and other informational engineering to data which has a geographical or geospatial aspect. And this typically involves applications capable of geospatial display and processing to get a compiled and useful data. "Learning Geospatial Analysis with Python" uses the expressive and powerful Python programming language to guide you through geographic information systems, remote sensing, topography, and more. It explains how to use a framework in order to approach Geospatial analysis effectively, but on your own terms. "Learning Geospatial Analysis with Python" starts with a background of the field, a survey of the techniques and technology used, and then splits the field into its component speciality areas: GIS, remote sensing, elevation data, advanced modelling, and real-time data. This book will teach you everything there is to know, from using a particular software package or API to using generic algorithms that can be applied to Geospatial analysis. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don't become bogged down in just getting ready to do analysis. "Learning Geospatial Analysis with Python" will round out your technical library with handy recipes and a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Summary


In this chapter we covered the foundations of remote sensing including:

  • Band swapping

  • Histograms

  • Image classification

  • Feature extraction

  • Change detection

As in the other chapters, we stayed as close to pure Python as possible, and where we compromised on this goal for processing speed, we limited the software libraries as much as possible to keep things simple. But, if you have the tools from this chapter installed, you really have a complete remote sensing package that is limited only by your desire to learn.

Tip

The authors of GDAL have a set of Python examples, which cover some advanced topics that may be of interest: http://svn.osgeo.org/gdal/trunk/gdal/swig/python/samples

In the next chapter we'll investigate elevation data. Elevation data doesn't fit squarely in GIS or remote sensing, as it has elements of both types of processing.