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Python Geospatial Analysis Cookbook

You're reading from   Python Geospatial Analysis Cookbook Over 60 recipes to work with topology, overlays, indoor routing, and web application analysis with Python

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781783555079
Length 310 pages
Edition 1st Edition
Languages
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Author (1):
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 Diener Diener
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Diener
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Table of Contents (20) Chapters Close

Python Geospatial Analysis Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
1. Setting Up Your Geospatial Python Environment FREE CHAPTER 2. Working with Projections 3. Moving Spatial Data from One Format to Another 4. Working with PostGIS 5. Vector Analysis 6. Overlay Analysis 7. Raster Analysis 8. Network Routing Analysis 9. Topology Checking and Data Validation 10. Visualizing Your Analysis 11. Web Analysis with GeoDjango Other Geospatial Python Libraries
Mapping Icon Libraries
Index

Converting a raster (GeoTiff) to a vector (Shapefile) using GDAL


We have now looked at how we can go from a vector to a raster, so it is now time to go from a raster to a vector. This method is much more common because most of our vector data is derived from remotely sensed data, such as satellite images, orthophotos, or some other remote sensing dataset, such as lidar.

Getting ready

As usual, enter the workon pygeoan_cb command in your Python virtual environment:

$ source venvs/pygeoan_cb/bin/activate

How to do it...

This recipe only requires four steps utilizing OGR and GDAL so please open up a new file for your code:

  1. Import the ogr and gdal modules and go straight ahead and open the raster we want to convert by passing it the filename on disk and getting a raster band:

    #!/usr/bin/env python
    # -*- coding: utf-8 -*-
    
    from osgeo import ogr
    from osgeo import gdal
    
    #  get raster data source
    open_image = gdal.Open( "../geodata/cadaster_borders-2tone-black-white.png" )
    input_band = open_image.GetRasterBand...
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