Raster processing using Python Tools: Reference

It’s best practice to create a unique conda environment for each of your projects. Here we install a raster environment called ‘rasterenv’.

Raster data is pixelated data where each pixel is associated with a specific location.

Raster data always has an extent and a resolution.

The extent is the geographical area covered by a raster.

The resolution is the area covered by each pixel of a raster.

Geospatial libraries such as GDAL are very useful for reading, writing and transforming rasters

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

Rasterio is built around the GDAL library (recall section 3), to facilitate raster operations in Python.

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

Computation is done in local memory on numpy arrays, then saved to the raster format of choice.

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