Nautical chart generalization is a tedious task and one of the most challenging and time consuming process in any Hydrographic Office (HO). Significant amounts of labor-intensive effort and time are needed for compiling, generalizing and maintaining those products (e.g., Electronic Navigation Charts ‘ENCs’). Accordingly, nowadays, one of the main objectives in many HOs is using automated generalization to achieve a perfect compromise for ENCs compilation in a cost-effective manner. However, regardless of the various relevant research efforts and the advancements in technology, generalization tasks are still performed mostly manually or semi-manually, where a likelihood of human error is admitted. Therefore, the ideal situation that would increase efficiency of data production at multiple scales and enable customized data products, is a fully automated generalization solution.  Such a solution would minimize the time and effort needed for ENC production and support rapid chart updates. In addition, it would solve many compilation problems by generating products on demand, at the right scale, at the point of use, and directly from the best available data respecting the main nautical chart constraints (i.e., Topology, Safety). For instance, since most available ENCs were originally compiled directly from the existing paper charts with digitization, following their traditional overlapping limits, inconsistency between adjacent ENCs are most likely encountered at the cells boundaries. These inconsistencies might affect the performance of the Electronic Chart Display Information System (ECDIS) that uses the data for analyses to the safety of the vessels underway. Thus, an automated generalization solution would significantly save time, and instead, HOs efforts can be steered toward compiling the largest scale cells with harmonizing data on the boundaries.

Towards this optimum goal, we present a research that aims to translate cartographic practice and theory into algorithmic building blocks that can iterate and cooperate to find the appropriate chart representation for any given area, at any scale, optimized according to set criteria. First of all, previous efforts for automated map production were investigated, and available nautical cartographic specifications were comprehensively reviewed. Secondly, generalization guidelines were extracted, categorized and translated into rules to be defined in a template as conditions to be respected during the generalization process. Lastly, an automated nautical generalization (AGN) model was developed to form a comprehensive process that utilizes; the generated template, as the input that derives the data generalization for any desired output scale, the source data, within the areas of interest, to perform the generalization to the target scale respecting topology constraints. However, since safety constraints cannot be ignored, a validation tool was developed that is capable of detecting all the safety violation spots in the output database.