October 2022
Beginner to intermediate
279 pages
6h 51m
English
Content preview from Python for Geospatial Data Analysis
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Chapter 9. Exploring the Geospatial Data Abstraction Library (GDAL)
No discussion of open source platforms and libraries would be complete without an introduction to the Geospatial Data Abstraction Library (GDAL), a resource for efficiently processing raster and vector geospatial data. Working with both raster and vector data requires a collection of tools, and GDAL operates under the hood of many of the programs you’ve used in this book, including ArcGIS, QGIS, and Google Earth. When relying on the programmatic ease of a graphical user interface, it is often easy to forget the complexity of combining diverse data types and formats in order to work efficiently with a unified data model. This is why GDAL is an important tool. It simplifies working with geospatial data across a wide range of formats and spatial reference systems.
In this chapter, we’ll work with rasters and look at how to use the Spyder IDE to work with GDAL, including how to change your map projection with the warp function, work with raster bands, transform files, and create binary masks. We’ll also do a quick orientation to three other useful resources for datasets to explore with GDAL: EarthExplorer, Copernicus Open Access Hub, and Google Earth Engine (GEE).
First, I’ll show you how to use the command line interface, also known as the terminal, to quickly read, transform, and reproject your geospatial data. Why the command line? If you’re working with multiple files that require the same functions, you don’t ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.