Abstract
Hydrographic sounding selection is the process of generalizing high-resolution bathymetry data to a more manageable subset capable of supporting nautical chart compilation or bathymetric modeling, and thus, is a fundamental task in nautical cartography. As technology improves and bathymetric data are collected at higher resolutions, the need for automated generalization algorithms that respect nautical cartographic constraints increases, since errors in this phase are carried over to the final product. Currently, automated algorithms for hydrographic sounding selection rely on radius- and grid-based approaches; however, their outputs contain a dense set of soundings with a significant number of cartographic constraint violations, thus increasing the burden and cost of the subsequent, mostly manual, cartographic sounding selection. This work presents a novel label-based generalization algorithm that utilizes the physical dimensions of the symbolized depth values on charts to avoid the over-plot of depth labels at scale. Additionally, validation tests based on cartographic constraints for nautical charting are implemented to compare the results of the proposed algorithm to radius and grid-based approaches. It is shown that the label-based generalization approach best adheres to the constraints of functionality (safety) and legibility.
Publication Date
4-25-2021
Journal Title
Cartography and Geographic Information Science
Rights
This work was authored as part of the Contributor’s official duties as an Employee of the United States Government and is therefore a work of the United States Government. Inaccordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Publisher
Taylor & Francis
Digital Object Identifier (DOI)
Document Type
Article
Recommended Citation
N. Dyer, Kastrisios, C., and De Floriani, L., “Label-Based Generalization of Bathymetry Data for Hydrographic Sounding Selection”, Cartography and Geographic Information Science, vol. 49(4). Taylor & Francis, 2022.
Comments
This is an open access article published by Taylor & Francis in Cartography and Geographic Information Science in 2021, available online: https://dx.doi.org/10.1080/15230406.2021.2014974