Book Image

Learning Geospatial Analysis with Python - Fourth Edition

By : Joel Lawhead
4 (1)
Book Image

Learning Geospatial Analysis with Python - Fourth Edition

4 (1)
By: Joel Lawhead

Overview of this book

Geospatial analysis is used in almost every domain you can think of, including defense, farming, and even medicine. In this special 10th anniversary edition, you'll embark on an exhilarating geospatial analysis adventure using Python. This fourth edition starts with the fundamental concepts, enhancing your expertise in geospatial analysis processes with the help of illustrations, basic formulas, and pseudocode for real-world applications. As you progress, you’ll explore the vast and intricate geospatial technology ecosystem, featuring thousands of software libraries and packages, each offering unique capabilities and insights. This book also explores practical Python GIS geospatial applications, remote sensing data, elevation data, and the dynamic world of geospatial modeling. It emphasizes the predictive and decision-making potential of geospatial technology, allowing you to visualize complex natural world concepts, such as environmental conservation, urban planning, and disaster management to make informed choices. You’ll also learn how to leverage Python to process real-time data and create valuable information products. By the end of this book, you'll have acquired the knowledge and techniques needed to build a complete geospatial application that can generate a report and can be further customized for different purposes.
Table of Contents (18 chapters)
1
Part 1:The History and the Present of the Industry
5
Part 2:Geospatial Analysis Concepts
11
Part 3:Practical Geospatial Processing Techniques

Understanding change detection

Change detection is the process of taking two geo-registered images of the same area from two different dates and automatically identifying differences. It is just another form of image classification. Just like our previous classification examples, it can range from trivial techniques like those used here to highly sophisticated algorithms that provide amazingly precise and accurate results.

For this example, we’ll use two images from a coastal area. These images show a populated area before and after a major hurricane, so there are significant differences, many of which are easy to visually spot, making these samples good for learning change detection. Our technique is to simply subtract the first image from the second to get a simple image difference using NumPy. This is a valid and often-used technique.

The advantages are that it is comprehensive and very reliable. The disadvantage of this overly simple algorithm is that it doesn’...