There are two parts to the georeferencing of raster datasets: the definition of the local, regional, or global system in which a raster’s pixels are located; and the parameters by which pixel coordinates are transformed into coordinates in that system.

Coordinate Reference System

The coordinate reference system of a dataset is accessed from its crs attribute.

>>> import rasterio
>>> src ='tests/data/RGB.byte.tif')
CRS({'init': 'epsg:32618'})

Rasterio follows pyproj and uses PROJ.4 syntax in dict form as its native CRS syntax. If you want a WKT representation of the CRS, see the CRS class’s wkt attribute.

'PROJCS["WGS 84 / UTM zone 18N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS 84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",-75],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32618"]]'

When opening a new file for writing, you may also use a CRS string as an argument.

>>> profile = {'driver': 'GTiff', 'height': 100, 'width': 100, 'count': 1, 'dtype': rasterio.uint8}
>>> with'/tmp/foo.tif', 'w', crs='EPSG:3857', **profile) as dst:
...     pass # write data to this Web Mercator projection dataset.

Coordinate Transformation

A dataset’s pixel coordinate system has its origin at the “upper left” (imagine it displayed on your screen). Column index increases to the right, and row index increases downward. The mapping of these coordinates to “world” coordinates in the dataset’s reference system is done with an affine transformation matrix.

>>> src.transform
Affine(300.0379266750948, 0.0, 101985.0,
       0.0, -300.041782729805, 2826915.0)

The Affine object is a named tuple with elements a, b, c, d, e, f corresponding to the elements in the matrix equation below, in which a pixel’s image coordinates are x, y and its world coordinates are x', y'.:

| x' |   | a b c | | x |
| y' | = | d e f | | y |
| 1  |   | 0 0 1 | | 1 |

The Affine class has some useful properties and methods described at

Some datasets may not have an affine transformation matrix, but are still georeferenced.

Ground Control Points

A ground control point (GCP) is the mapping of a dataset’s row and pixel coordinate to a single world x, y, and optionally z coordinate. Typically a dataset will have multiple GCPs distributed across the image. Rasterio can calculate an affine transformation matrix from a collection of GCPs using the rasterio.transform.from_gcps method.

Rational Polynomial Coefficients

A dataset may also be georeferenced with a set of rational polynomial coefficients (RPCs) which can be used to compute pixel coordinates from x, y, and z coordinates. The RPCs are an application of the Rigorous Projection Model which uses four sets of 20 term cubic polynomials and several normalizing parameters to establish a relationship between image and world coordinates. RPCs are defined with image coordinates in pixel units and world coordinates in decimal degrees of longitude and latitude and height above the WGS84 ellipsoid (EPSG:4326).

RPCs are usually provided by the dataset provider and are only well behaved over the extent of the image. Additionally, accurate height values are required for the best results. Datasets with low terrain variation may use an average height over the extent of the image, while datasets with higher terrain variation should use a digital elevation model to sample height values.The coordinate transformation from world to pixel coordinates is exact while the reverse is not, and must be computed iteratively. For more details on coordinate transformations using RPCs see