Book Image

Learning Geospatial Analysis with Python

By : Joel Lawhead
Book Image

Learning Geospatial Analysis with Python

By: Joel Lawhead

Overview of this book

Geospatial Analysis is used in almost every field you can think of from medicine, to defense, to farming. This book will guide you gently into this exciting and complex field. It walks you through the building blocks of geospatial analysis and how to apply them to influence decision making using the latest Python software. Learning Geospatial Analysis with Python, 2nd Edition uses the expressive and powerful Python 3 programming language to guide you through geographic information systems, remote sensing, topography, and more, while providing a framework for you to approach geospatial analysis effectively, but on your own terms. We start by giving you a little background on the field, and a survey of the techniques and technology used. We then split the field into its component specialty areas: GIS, remote sensing, elevation data, advanced modeling, and real-time data. This book will teach you everything you need to know about, Geospatial Analysis from using a particular software package or API to using generic algorithms that can be applied. This book focuses on pure Python whenever possible to minimize compiling platform-dependent binaries, so that you don’t become bogged down in just getting ready to do analysis. This book will round out your technical library through handy recipes that will give you a good understanding of a field that supplements many a modern day human endeavors.
Table of Contents (17 chapters)
Learning Geospatial Analysis with Python Second Edition
About the Author
About the Reviewers

Chapter 6. Python and Remote Sensing

In this chapter, we will discuss remote sensing. This field grows more exciting everyday as more satellites are launched and the distribution of data becomes easier. The high availability of satellite and aerial images as well as interesting new types of sensors launching each year is changing the role that remote sensing plays in understanding our world.

In this field, Python is quite capable. However, in this chapter, we will rely more on the Python bindings to the C libraries than we have in the previous chapters, where the focus was more on using pure Python. The only reason for this change is the size and complexity of remotely-sensed data. In remote sensing, we step through each pixel in an image and perform some form of query or mathematical process. An image can be thought of as a large numerical array. In remote sensing, these arrays can be quite large to the order of tens of megabytes to several gigabytes. While Python is fast, only C-based libraries...