## 3 Results  
The developed MultiGeoref software [28] was used for georeferencing our area of interest, and 250 map sheets were adjusted in 4 variants. The second-order polynomial and affine transformations were tested, both in the classical version with an LSM adjustment and in the IRLS version with [PERSON]'s estimation. The results of the resulting mean errors and the number of iterations are shown in Table 1.  
It is obvious that the use of IRLS leads to the reduction of the resulting RMSE, in the case of the polynomial transformation even quite significantly. The resulting error of 280 m is at a very good level according to the parameters of the map creation. The results differ from those published in [28], mainly due to different contamination settings of the input measurements. Furthermore, a different number of map sheets were used (two were discarded) and with a different set of GCPs (some were also discarded or modified after the inspection). The distributions of the resulting deviations (corrections) on GCPs are shown in Figure 4. The maximum frequencies reach deviations in the range from 100 m to 300 m.  
The IRLS method used, together with the robust [PERSON]'s estimate, provides an estimate of the adjusted characteristic, which is, compared to the classical LSM, not unbiased but is less affected by completely incorrectly determined GCPs. The disadvantage is also the higher computational complexity of the method. The comparison of the computational time of georeferencing using classical LSM and IRLS is described in [28]. The resulting image data in the mosaic show proper characteristics and usually copy the rectangular shape of map sheets. Some edge sheets are significantly deformed because they contain only a small area covered by GCPs. The rest of the sheet is, thus, significantly affected, especially with the polynomial transformation used, by the need to extrapolate outside the area covered by the data (see Figure 5). Problems can also be caused by a sparse population and generally not a very rich drawing in some peripheral parts of Bohemia (especially in northern, northeastern and southwestern mountainous areas), which makes it difficult to find GCPs; their lack has consequently a secondary effect on the result of georeferencing. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Transformation** & **Affine LSM** & **Affine IRLS** & **Polynomial 1** & **LSM** & **Polynomial 1** & **IRLS** \\ \hline RMSE & 1335 m & 1043 m & 390 m & 280 m \\ No. of iterations & \(1\) & 13 & 1 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of georeferencing. Figure 3: Breakages of line objects in the map mosaic on Mapire.eu. As part of testing the possibility of introducing weights by analyzing the behavior of mean errors of individual adjusted coordinates in test areas in northern Bohemia and their classification into individual defined categories, the existence of a correlation between this categorization and the achieved mean errors was not reliably proven. A higher level of accuracy of the representation of churches and other fixed, important objects (farmsteads, chateaux, yards, mills) has been demonstrated, but assessing the accuracy of road crossings and other \"lower\" types of GCPs is difficult due to their great diversity and, most likely, a lower priority of the geometric accuracy of their rendition (they could often have been a schematic \"background\", mere iconographic symbols, complementing the substantial content of maps). Examples of collected GCPs with their position on the OpenStreetMap can be seen on Figure 6.  
Figure 4: Histogram of distribution of deviations (in meters) on GCPs after the adjustment (polynomial, IRLS). Figure 5: Detail of the deformed shape of several peripheral map sheets. The accuracy of the quantities entering within individual categories of GCPs can be roughly estimated according to the following Table 2 (the weight of churches represents the value of 1 and the other weights are related to it).