Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying  Learning Geospatial Analysis with Python
  • Table Of Contents Toc
 Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python - Fourth Edition

By : Joel Lawhead
5 (7)
close
close
 Learning Geospatial Analysis with Python

Learning Geospatial Analysis with Python

5 (7)
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)
close
close
1
Part 1:The History and the Present of the Industry
5
Part 2:Geospatial Analysis Concepts
11
Part 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 figure shows 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:

Figure 7.9 – Key features in a thermal satellite image

Figure 7.9 – Key features in a thermal satellite image

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 to simplify...

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
 Learning Geospatial Analysis with Python
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon