@article{Nada2026, 
author = {Tamer Nada and Christos Kastrisios and Brian Calder and Christie Ence and Craig Greene and Amber Bethell},
title = {Automated islands generalization techniques for nautical charts},
year = {2026},
journal = {Geo-Spatial Information Science},
volume = {29},
number = {4},
pages = {2670-2683},
keywords = {ENC compilation, automated generalization, island selection, isolated islands, land-area generalization},
url = {https://www.sciopen.com/article/10.1080/10095020.2025.2568115},
doi = {10.1080/10095020.2025.2568115},
abstract = {Land is one of the most prominent geo-features in marine navigation for passage planning and execution. Land generalization for nautical charting, and particularly that of smaller landmasses (islands), is a task performed manually by cartographers based on available specifications and their own experience. Existing methods for automated island selection utilize the Voronoi diagram or weighted buffers to calculate island density and remove islands in dense regions. Their main deficiency is that they do not consider other related chart features, while they may also incorrectly represent the density and distribution of islands and the detection of isolated ones in the process. As such, islands that pose navigational significance and would be retained by a professional cartographer may be removed by the algorithm and vice versa. These drawbacks restrict their use as they present difficulties in achieving the product requirements. This paper presents an island generalization method that combines the benefits of Voronoi and buffer approaches, while it incorporates relevant other chart feature classes to respect topology relations and, with island shape recognition, to categorize islands on a hierarchy level for making generalization decisions. This innovative approach enhances the effectiveness and accurate presentation of islands on nautical charts through automated generalization.}
}