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Learning Geospatial Analysis with Python-Second Edition

You're reading from   Learning Geospatial Analysis with Python-Second Edition An effective guide to geographic information systems and remote sensing analysis using Python 3

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Product type Paperback
Published in Dec 2015
Publisher Packt
ISBN-13 9781783552429
Length 394 pages
Edition 1st Edition
Languages
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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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Toc

Table of Contents (12) Chapters Close

Preface 1. Learning Geospatial Analysis with Python FREE CHAPTER 2. Geospatial Data 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 Modeling 9. Real-Time Data 10. Putting It All Together Index

GDAL


GDAL is the dominant geospatial library for raster data. 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 this. 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://github.com/GeospatialPython/Learn/raw/master/SatImage.zip. Let's open this image and see how many bands it has and how many pixels are present along each axis:

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

So, we see that the following image has three bands, 2,592 columns of pixels, and 2,693 rows of pixels by viewing it in OpenEV:

GDAL is an extremely fast geospatial raster reader and writer within Python. It can also reproject images quite well in addition to a few other tricks. However, the true value of...

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