Raster processing using Python Tools: Reference

Key Points

Introduction: Working with Raster Data
Geospatial Concepts: Raster Data
  • Gridded data that vary in space and time are common in many geospatial applications

  • Specialized tools are needed to accommodate the complexity and size of many raster datasets

  • Some image formats have been adapted to store spatial metadata

Encodings, Formats and Libraries
  • Geospatial libraries are very useful for reading, writing and transforming geospatial rasters

  • Once a raster’s pixel values have been extracted to a NumPy array, they can be processed with more specialized libraries and routines.

Working with Raster Datasets
  • GDAL is a ‘swiss army knife’ for raster operations, but is pretty low-level.

  • Rasterio provides much of GDAL’s functionality and is easier to install, but supports fewer formats out of the box.

  • Pixel values of rasters can be extracted to a numpy array.

  • Raster analysis in python revolves around numpy arrays as the primary data structure and programming model.

Rainier DEM Example
  • pygeotools provides functions built on GDAL to simplify many raster warping tasks

  • Raster analysis in Python revolves around NumPy arrays as the primary data structure and programming model

FIXME: more reference material.