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QGIS Python Programming Cookbook, Second Edition

QGIS Python Programming Cookbook, Second Edition

By : Joel Lawhead
1.5 (2)
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QGIS Python Programming Cookbook, Second Edition

QGIS Python Programming Cookbook, Second Edition

1.5 (2)
By: Joel Lawhead

Overview of this book

QGIS is a desktop geographic information system that facilitates data viewing, editing, and analysis. Paired with the most efficient scripting language—Python, we can write effective scripts that extend the core functionality of QGIS. Based on version QGIS 2.18, this book will teach you how to write Python code that works with spatial data to automate geoprocessing tasks in QGIS. It will cover topics such as querying and editing vector data and using raster data. You will also learn to create, edit, and optimize a vector layer for faster queries, reproject a vector layer, reduce the number of vertices in a vector layer without losing critical data, and convert a raster to a vector. Following this, you will work through recipes that will help you compose static maps, create heavily customized maps, and add specialized labels and annotations. As well as this, we’ll also share a few tips and tricks based on different aspects of QGIS.
Table of Contents (10 chapters)
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Classifying a raster


Image classification is one of the most complex aspects of remote sensing. While QGIS is able to color pixels based on values for visualization, it stops short of doing much classification. It does provide a Raster Calculator tool where you can perform arbitrary math formulas on an image; however, it does not attempt to implement any common algorithms. The Orfeo Toolbox is dedicated purely to remote sensing and includes an automated classification algorithm called K-Means Clustering, which groups pixels into an arbitrary number of similar classes to create a new image. We can do a nice demonstration of image classification using this algorithm.

Getting ready

For this recipe, we will use a false color image, which you can download here:

https://github.com/GeospatialPython/Learn/raw/master/FalseColor.zip

Unzip this TIF file and place it in your /qgis_data/rasters directory.

How to do it...

All we need to do is run the algorithm on our input image. The important parameters...

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