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Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python If you know Python and would like to use it for Geospatial Analysis this book is exactly what you've been looking for. With an organized, user-friendly approach it covers all the bases to give you the necessary skills and know-how.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783281138
Length 364 pages
Edition 1st Edition
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Author (1):
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Joel Lawhead Joel Lawhead
Author Profile Icon Joel Lawhead
Joel Lawhead
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Table of Contents (12) Chapters Close

Preface 1. Learning Geospatial Analysis with Python 2. Geospatial Data FREE CHAPTER 3. The Geospatial Technology Landscape 4. Geospatial Python Toolbox 5. Python and Geographic Information Systems 6. Python and Remote Sensing 7. Python and Elevation Data 8. Advanced Geospatial Python Modelling 9. Real-Time Data 10. Putting It All Together Index

GDAL


GDAL is the dominant geospatial library. Its raster capability is so significant that it is a part of virtually every geospatial toolkit in any language and Python is no exception. To see the basics of how GDAL works in Python, download the following sample raster satellite image as a ZIP file and unzip it: https://geospatialpython.googlecode.com/files/SatImage.zip

Let's open this image and see how many bands it has and how many pixels along each axis:

>>> from osgeo import gdal
>>> raster = gdal.Open("SatImage.tif")
>>> raster.RasterCount
3
>>> raster.RasterXSize
2592
>>> raster.RasterYSize
2693

So we see this image has three bands, 2,592 columns of pixels, and 2,693 rows of pixels, as shown in OpenEV:

GDAL is an extremely fast geospatial raster reader and writer within Python. It can also reproject images quite well plus a few other tricks. However, the true value of GDAL comes from its interaction with the next Python module that we'll examine...

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