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Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python Understand GIS fundamentals and perform remote sensing data analysis using Python 3.7

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Product type Paperback
Published in Sep 2019
Publisher
ISBN-13 9781789959277
Length 456 pages
Edition 3rd Edition
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Author (1):
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Joel Lawhead Joel Lawhead
Author Profile Icon Joel Lawhead
Joel Lawhead
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Table of Contents (15) Chapters Close

Preface 1. Section 1: The History and the Present of the Industry FREE CHAPTER
2. Learning about Geospatial Analysis with Python 3. Learning Geospatial Data 4. The Geospatial Technology Landscape 5. Section 2: Geospatial Analysis Concepts
6. Geospatial Python Toolbox 7. Python and Geographic Information Systems 8. Python and Remote Sensing 9. Python and Elevation Data 10. Section 3: Practical Geospatial Processing Techniques
11. Advanced Geospatial Python Modeling 12. Real-Time Data 13. Putting It All Together 14. Other Books You May Enjoy

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...

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