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

Calculating satellite image cloud cover

Satellite images give us a powerful bird's-eye view of the Earth. They are useful for a variety of purposes, which we saw in Chapter 6, Python and Remote Sensing. However, they have one flaw—clouds. As a satellite passes around the Earth and collects imagery, it inevitably images clouds. And in addition to obstructing our view of the Earth, the cloud data can adversely affect remote sensing algorithms by wasting CPU cycles on useless cloud data, or skew the results by introducing unwanted data values.

The solution is to create a cloud mask. A cloud mask is a raster that isolates the cloud data in a separate raster. You can then use that raster as a reference when processing the image in order to avoid cloud data, or you can even use it to remove the clouds from the original image.

In this section, we'll create a cloud mask...