Digital bottom models are commonly used in many fields of human activity, such as navigation, harbor and offshore technologies, or environmental studies. In many cases, they are the basis for further analysis. They are prepared based on bathymetric measurements, which in many cases have the form of large datasets. Therefore, various interpolation methods are used for calculating these models. In this paper, we present the analysis in which we compared selected methods for bottom surface modeling with a particular focus on geostatistical methods. The aim was to compare five variants of Kriging and three deterministic methods. The research was performed with real data acquired with the use of an autonomous surface vehicle. The collected bathymetric data were reduced (from about 5 million points to about 500 points) and analyzed. A ranking approach was proposed to perform a complex and comprehensive analysis integrating typically used error statistics—mean absolute error, standard deviation and root mean square error. This approach allowed the inclusion of various views on methods of assessment while integrating various metrics and factors. The results show that geostatistical methods perform very well. The best results were achieved with the modifications of classical Kriging methods, which are disjunctive Kriging and empirical Bayesian Kriging. For these two methods, good statistics were calculated compared to other methods (for example, the mean absolute error for disjunctive Kriging was 0.23 m, while for universal Kriging and simple Kriging, it was 0.26 m and 0.25 m, respectively). However, it is worth mentioning that interpolation based on radial basis function in some cases is comparable to Kriging in its performance. The proposed ranking approach was proven to be useful and can be utilized in the future for choosing and comparing DBMs, mostly in mapping and analyzing seabed changes, for example in dredging operations. The research will be used during the implementation of the new multidimensional and multitemporal coastal zone monitoring system using autonomous, unmanned floating platforms. The prototype of this system is at the design stage and is expected to be implemented.

The aim of this study was to develop a comprehensive methodology for assessing the effectiveness of conceptual navigation maps. To achieve this, a set of indicators was developed to simplify and standardise the evaluation process. A key challenge in analysing the effectiveness of maps is the variety of ways to assess their effectiveness (eye-tracking measures, visual interpretation, questionnaires), which often leads to ambiguous interpretations. In the initial phase, three separate indicators were established: readability of the map, correctness of map symbol identification, and the time required to identify these objects. For the readability indicator, a correlation analysis with eye-tracking measurements and a heatmap decomposition was carried out, which partly reduced the complexity of the data. This led to the development of a single universal indicator, representing the overall effectiveness of the map in numerical form and allowing a simple comparison of maps. Based on the methodology developed, we were able to conclude that the designed 3D concept maps are more effective than their 2D counterparts. The methodology and universal indicator presented in this study can be applied in practice to evaluate entire series of concept maps and provide designers—including those outside academia—with indicator-based tools to evaluate the effectiveness of maps.