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

Working with LiDAR data

LiDAR stands for Light Detection and Ranging. It is similar to radar-based images but uses finite laser beams that hit the ground hundreds of thousands of times per second to collect a huge amount of very fine (x, y, z) locations, as well as time and intensity. The intensity value is what really separates LiDAR from other data types. For example, the asphalt rooftop of a building may be of the same elevation as the top of a nearby tree, but the intensities will be different. Just like remote sensing, radiance values in a multispectral satellite image allow us to build classification libraries. The intensity values of LiDAR data allow us to classify and colorize LiDAR data.

The high volume and precision of LiDAR actually make it difficult to use. A LiDAR dataset is referred to as a point cloud because the shape of the dataset is usually irregular as the data is three-dimensional with outlying points. There are not many software packages that effectively visualize...