For large raster datasets, the pangeo project may be of interest. There is a wide variety of raster data processing methods in Python, given the extensive ecosystem of satellite imagery and analysis tools for Python. Reading & Writing raster data using Rasterio This is opposed to vector data, which grew out of applications in computer graphics.īelow, we discuss a few methods for working with rasters, as well as ways to combine rasters. In geography, this terminology comes from early work in digital aerial photography applications, where each band represented an electromagnetic wavelength (such as infrared, red, green, blue, ultraviolet, etc.) recorded by an aerial sensor.
If there are multiple sensed values at each site, then each value is assigned a band. Usually, Raster datasets map naturally to an array, where each element is the observed value at a given cell. Raster representations of geographic data are like images they are composed of small identical polygons (usually squares) with a single value. In the previous notebook, we worked mainly with vector geometries, although the basemaps we used to construct better maps were raster representations of geographical data. These combinations have certain rules for how they can be combined into geometries. Coordinates have no dimension, but are combined togehter to form geometries, such as points, lines, and polygons. The first, called vector data, refers to a representation where coordinates are indexed in continuous space with a position vector.
Geographic data comes in two common representations.
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