coordinate system ( = pixel column, = pixel row) and the ground coordinate system (). Easily visible locations that be precisely located are preferred as GCP's, such as a road intersection or the corner of a building. When very high accuracy registration is required, it is common to place or paint high-contrast markers on the ground at survey control monuments before the photography is taken, and use GNSS-measured coordinates for the output. In most software, these are entered by pointing at the location on the image, then pointing at the same location on a vector base map or orthophoto that is already in the desired coordinate system. This can then be moved and adjusted to improve accuracy. With a minimal set of GCPs, the known coordinates can be entered into the mathematical equations for the desired type of transformation, which can then be solved using linear algebra to determine the coefficients and derive the formulas to use for the entire grid. For example, the linear affine transformation above has six unknown coefficients, so six equations with known <> are needed to derive them, which will require three ground control points. The second-order polynomial requires a minimum of six ground control points, and so on. The entered GCPs are rarely perfectly located and are even more rarely perfectly representative of the distortion in the rest of the image, but the algebraic solution, which appears to be a perfect match, masks any error. To avoid this, it is common to create many more than the minimal required set (creating an overdetermined system), and use least squares regression to derive a set of function parameters that most closely matches the points. This is almost never a perfect match, so the variance between each GCP location and the location predicted by the functions can be measured and summarized as a Root-mean-square error (RMSE). A lower RMSE thus means that the transformation formulas closely match the GCPs. Once the function parameters are determined, the transformation functions can be used to transform every pixel of the image to its real-world location. Two options are usually available for making this transformation permanent. One option is to save the parameters themselves as a form of metadata, either in the header of the image file itself (e.g., GeoTIFF), or in a sidecar file stored alongside the image file (e.g., a world file). With this metadata, the software can perform