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

Extracting features from images

The ability to classify an image leads us to another remote sensing capability. Now that you’ve worked with shapefiles over the last few chapters, have you ever wondered where they come from? Vector GIS data such as shapefiles are typically extracted from remotely sensed images such as the examples we’ve seen so far.

Extraction normally involves an analyst clicking around each object in an image and drawing the feature to save it as data. But with good remotely-sensed data and proper pre-processing, it is possible to automatically extract features from an image.

For this example, we’ll take a subset of our Landsat 8 thermal image to isolate a group of barrier islands in the Gulf of Mexico. The islands appear white as the sand is hot and the cooler water appears black (you can download this image from https://github.com/PacktPublishing/Learning-Geospatial-Analysis-with-Python-Fourth-Edition/raw/main/B19730_07_Asset_Files/islands...