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Geoprocessing with Python by Chris Garrard

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Chapter 9. Reading and writing raster data

This chapter covers

  • Understanding raster data basics
  • Introducing GDAL
  • Reading and writing raster data
  • Resampling data

If you have a geographic dataset that’s made of continuous data such as elevation or temperature, it’s probably a raster dataset. Spectral data such as aerial photographs and satellite imagery are also stored this way. These types of datasets don’t assume strict boundaries exist between objects in the way that vector datasets do. Think of a digital photograph and how each pixel can be a slightly different color than the pixels next to it. The fact that pixel values can vary continuously like this makes for a much better-looking photo than if there were only a few colors to choose ...

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