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Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python If you know Python and would like to use it for Geospatial Analysis this book is exactly what you've been looking for. With an organized, user-friendly approach it covers all the bases to give you the necessary skills and know-how.

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Product type Paperback
Published in Oct 2013
Publisher Packt
ISBN-13 9781783281138
Length 364 pages
Edition 1st 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 (12) Chapters Close

Preface 1. Learning Geospatial Analysis with Python 2. Geospatial Data FREE CHAPTER 3. The Geospatial Technology Landscape 4. Geospatial Python Toolbox 5. Python and Geographic Information Systems 6. Python and Remote Sensing 7. Python and Elevation Data 8. Advanced Geospatial Python Modelling 9. Real-Time Data 10. Putting It All Together Index

Working with LIDAR


LIDAR stands for Light Detection and Ranging. It is similar to radar-based images but uses finite laser beams, which 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, but the asphalt roof top of a building may be the same elevation as the top of a nearby tree, the intensities will be different. And 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 as well.

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

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