Book Image

Learning Geospatial Analysis with Python - Third Edition

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python - Third Edition

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. With this systematic guide, you'll get started with geographic information system (GIS) and remote sensing analysis using the latest features in Python. This book will take you through GIS techniques, geodatabases, geospatial raster data, and much more using the latest built-in tools and libraries in Python 3.7. You'll learn everything you need to know about using software packages or APIs and generic algorithms that can be used for different situations. Furthermore, you'll learn how to apply simple Python GIS geospatial processes to a variety of problems, and work with remote sensing data. By the end of the book, you'll be able to build a generic corporate system, which can be implemented in any organization to manage customer support requests and field support personnel.
Table of Contents (15 chapters)
Free Chapter
Section 1: The History and the Present of the Industry
Section 2: Geospatial Analysis Concepts
Section 3: Practical Geospatial Processing Techniques

Classifying images

Automated remote sensing (ARS) is rarely ever done in the visible spectrum. ARS processes images without any human input. The most commonly available wavelengths outside of the visible spectrum are infrared and near-infrared.

The following illustration is a thermal image (band 10) from a fairly recent Landsat 8 flyover of the US Gulf Coast from New Orleans, Louisiana to Mobile, Alabama. The major natural features in the image have been labeled so that you can orient yourself:

Because every pixel in that image has a reflectance value, it is information as opposed to just color. The type of reflectance can tell us definitively what a feature is, as opposed to us guessing by looking at it. Python can see those values and pick out features the same way we intuitively do by grouping related pixel values. We can colorize pixels based on their relation to each other...