## 4 Discussion  
As we have mentioned in the Introduction, most studies published on the topic of geodetic network optimization, although mathematically often correct, do not provide results that could be directly applied in the geodetic practice as they do not take into account the aforementioned basic principles of geodetic practice. [PERSON] studied the designs of both the first and second orders [15]. He simultaneously optimized the position of both points and weights of the measurements, combining the accuracy and reliability criteria. In addition to this, he introduced a new criterion--sensitivity--describing the ability to detect the displacements occurring between stages of measurement. However, the results of the presented simulated examples were not applicable in practice; similarly to Berne and Baselga [11], the network points converged to predefined borders or were often in impossible locations (e.g., high above the ground or below the terrain). When applying these theoretical findings to the monitoring of an existing dam (employing SOD), the optimization led to a significant reduction in the measurement weights (representing repetitions) while maintaining the required accuracy. Unfortunately, the use of continuous values for weights is suboptimal as when calculating the required numbers of measurements, the calculation returns non-integer numbers. Despite these shortcomings, [PERSON] and [PERSON] [11], the authors showed that the proposed method is able to detect the displacements occurring between stages of measurement and the measurement. Figure 5: Changes in the performance (mean normalized performance, MNP) of individual algorithms as a function of the total station accuracy for **(a)** the square-like network, **(b)** triangular network, **(c)** bridge network, and **(d)** building network. followed up on this work [13], using FOD for the optimization of a geodetic network of a mine. Again, however, the optimized numbers ended up on the predefined borders. [PERSON] worked on SOD in several papers. In [16], he and his team optimized the weights in the geodetic network with an objective function for reliability. In their follow-up work [17], they optimized the network in a way ensuring that all measurements contributed equally to the network accuracy. Both studies work with the measurement weights, which are not directly applicable to real-world measurements. [PERSON] et al. [18] used a heuristic method of particle swarm optimization (see [19] for details) for the second-order optimization of network measurements using global navigation satellite systems (GNSSs). In that study, the work with weights may make more sense than when the total station is used for measurement as the weights of measurement change almost continuously with the observation duration. In this paper, we have proposed and tested a practically applicable automated method for SOD geodetic network optimization. The proposed method consists of a battery of algorithms that are applied in parallel and, subsequently, the best one is automatically selected. The only inputs for this method comprise the approximate coordinates of the points of the geodetic network, the information about what measurements can be taken in reality, the accuracy of the total station, and the required resulting accuracy of the geodetic network. In this context, we define the optimal network as one with all points measured with an accuracy parameter equal to or better than a set value obtained using the minimum effort (i.e., number of measurements). The ease of practical applicability is especially given by the use of integer numbers of measurements and by considering the measurements taken at the same moment using the total station as a single group of measurements (which exactly corresponds to the practical terrain measurements) and by having all measurements from a single standpoint taken for an equal number of times.