and mapping activities. of particular importance is the densification of a geodetic network. In that case, new geodetic networks are established based on an existing network. In other words, new points are often tied to previously established control points. If we want to determine the coordinates of that new points, measurements based on directions, distances, height differences and/or space-based geodetic techniques, such as Global Navigation Satellite Systems (GNSS), should be performed between the new points and the existing control points (now we just refer \"_existing control points_\" to as _control points_). In most cases of geodetic networks, the measurements bear a known linear relationship with the unknown parameters. In other words, the mathematical model that relates measurements to parameters is linear. In that case, the coordinates of new points are unknown parameters, whereas the coordinates of control points are constraints on the mathematical model. Details about constraints can be verified in [2]. The coordinates of the new points are often computed from the Least-Squares Estimation (LSE). In other words, we seek to find the coordinates of the new points that minimises the sum-of-squares of the residuals. The residuals is the difference between the observed values and the estimated (or expected) values of the measurements [3; 4; 5]. A step prior to the deployment of a geodetic network is the Geodetic Network Design. The quality of a geodetic network is characterised by its precision, reliability, and cost [6; 7]. The covariance matrix of the least-squares parameters is associated with the degree of the network precision. There is also the internal and external reliability of a geodetic network, which were both introduced by [4]. Internal reliability refers to the ability of a network to detect outliers in the set of observations, and external reliability refers to the effect of the undetectable outliers on the estimated parameters. Here, outlier is defined according to [8]: \"_An outlier is an measurement that is so probably caused by a gross error that it is better not used or not used as it is\"_. The cost is related to the effort required to implement the design and related expenses. There is not the best geodetic network, but the optimal geodetic network. Designing a geodetic network in the optimal sense involves designing an optimal configuration for the network (e.g., selection of the position of network control points). Optimal design problem of a geodetic network was widely investigated since the pioneering work of [9; 10]. Since then, several works were described in detail in papers published in scientific journals (see e.g., [1; 6; 7; 11; 12; 13; 14; 15; 16; 17; 18; 19]). Details about the orders of design is outside of discussion in this paper. However, details can be found in [10; 11]. In this paper, we provide an automatic procedure to design a geodetic network in terms of reliability. Here, we use the reliability theory for designing geodetic networks. The reliability theory becomes a fundamental part of modern data analysis [1; 20; 21; 22; 23]. This is due to [3; 4]. Generally, the random measurement errors in a system are unavoidable. However, the presence of an outlier in the dataset can jeopardise the reliability level of the system. To detect a possible outlier, [PERSON] [4] introduced the Data Snooping procedure for the detection of a single outlier in linear(ised) models. Most of conventional geodetic studies have a chapter on [PERSON]'s data snooping procedure, e.g., [5; 24]. Data snooping has become very popular and is routinely used in adjustment computations [25]. Although introduced in the context of geodetic networks, Data Snooping is a generally applicable method of outlier detection in both univariate and multivariate approaches [26]. Data snooping is a procedure based on hypothesis testing, which consists of screening each individual observation for a possible outlier. The test statistic associated with Data Snooping is given by a normalised least-squares residuals.