## 1 Introduction
The differences between projections are, in fact, at the graphic level of erroneous GCPs which are utilized for the calculation. Therefore, if we do not know the projection and cannot estimate it, it is possible to work with the most probable variant. Thus, for example, [PERSON]'s projection was used to georeference a map of France [12]. If this most probable variant still does not correspond exactly to the processed maps, it is possible to add a second step by some of the interpolation methods, e.g., grid shift binary (GSB) as in [9]. Georeferencing can also be used without the knowledge of the projection used. Then, it is necessary to work with GCPs coordinates and use one of the transformation methods. A large group of global methods (similarity, affine, polynomial transformations) and/or local transformations using interpolation mechanisms (spline, IDW) is offered. The division of transformation methods is described in [13]. Among the global methods, an affine transformation is very often used. However, if the cartographic projection of the old map and the reference coordinate system are significantly different, it is necessary to use more complex polynomial transformations. They also successfully reduce errors at GCPs after the transformation. The testing of the degrees of polynomial transformations for georeferencing is performed in [14]. It is important to note that the least squares method (LSM) is usually used to calculate global transformation methods [15]. To calculate the adjustment correctly, there is a need to know how individual GCPs and their surroundings behave after the transformation. It is especially important to evenly and continuously cover the transformed area with GCPs [16]. When balancing using the least squares method, it is also possible to consider assigning weights to GCPs based on their characteristics. This is addressed in [17]. A standard adjustment using LSM has the disadvantage of not being able to reveal erroneous or poor-quality measurements. The GCP which is subject to an error and is not detected affects the adjustment results. Therefore, it is good to consider statistical methods that are able to filter out these measurements or suppress their influence. There are many statistical methods that can test residuals at GCPs. We can mention [PERSON]'s method of testing of maximal residual based on known standard deviation. Another possibility can be [PERSON]'s test or testing using mean residual. The use of statistical tests for GCPs or the use of a robust adjustment based on the maximum likelihood method, the so-called M-estimator [18], can be recommended. For local transformation methods, transformations based on a radial function are used. Spline transformations are the most used, namely the thin plate spline (TPS) method [19]. However, it is possible to use other interpolations. Their common feature is non-residuality on GCPs and thus image distortion. Therefore, it is important to have a relatively large number of GCPs in the map drawing so that excessive deformations do not occur in areas not covered by GCPs. An alternative to using interpolation methods is to transform the image in parts (triangles, quadrilaterals) [20]. The division of the old map image into triangles and their separate transformations is called rubber-sheeting [21]. However, the disadvantage of these procedures may be visible edges between individual parts. For example, line elements are bent on them. Choosing a particular transformation method is a very difficult task. The comparison of the standard polynomial transformation and the method of rubber-sheeting can be found in [22]. A comparative study of transformations when applied to historical map of France is presented in [23]. The distribution and the number of GCPs and the use of kernel-based methods are discussed. The reliability of individual GCPs and thus the assignment of different weights is also used.