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 Learning Geospatial Analysis with Python

You're reading from   Learning Geospatial Analysis with Python Unleash the power of Python 3 with practical techniques for learning GIS and remote sensing

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781837639175
Length 432 pages
Edition 4th 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 (18) Chapters Close

Preface 1. Part 1:The History and the Present of the Industry
2. Chapter 1: Learning about Geospatial Analysis with Python FREE CHAPTER 3. Chapter 2: Learning about Geospatial Data 4. Chapter 3: The Geospatial Technology Landscape 5. Part 2:Geospatial Analysis Concepts
6. Chapter 4: Geospatial Python Toolbox 7. Chapter 5: Python and Geospatial Algorithms 8. Chapter 6: Creating and Editing GIS Data 9. Chapter 7: Python and Remote Sensing 10. Chapter 8: Python and Elevation Data 11. Part 3:Practical Geospatial Processing Techniques
12. Chapter 9: Advanced Geospatial Modeling 13. Chapter 10: Working with Real-Time Data 14. Chapter 11: Putting It All Together 15. Assessments 16. Index 17. Other Books You May Enjoy

What is metadata?

As discussed in Chapter 1, Learning about Geospatial Analysis with Python, metadata is any data that describes the associated dataset. Common examples of metadata include basic elements such as the footprint of the dataset on Earth, as well as more detailed information such as spatial projection and information describing how the dataset was created.

Most data formats contain the footprint or bounding box of the data on Earth. Detailed metadata is typically stored in a separate location in a standard format, such as the US Federal Geographic Data Committee (FGDC), Content Standard for Digital Geospatial Metadata (CSDGM), ISO, or the newer European Union initiative, which includes metadata requirements, and is called the Infrastructure for Spatial Information in the European Community (INSPIRE).

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