An Automated Nautical Chart Generalization Model

Abstract

Significant amounts of labor-intensive effort and time are needed for compiling and maintaining Electronic Navigation Charts (ENCs). The great amount of data collected with high-resolution systems that are being delivered to the charting divisions along with initiatives within the divisions, such as the Office of Coast Survey (OCS)/ Marine Chart Division's (MCD) rescheme project, often lead to a bottleneck situation. Therefore, nowadays, one of the main objectives in many Hydrographic Offices (HOs) is automating generalization tasks to improve productivity in a cost-effective manner. However, regardless of the various relevant research efforts and the advancements in technology, most generalization tasks are still performed manually or semi-manually, where a likelihood of human error is admitted. The ideal situation would be a fully automated solution which could minimize the time and effort needed for ENC production and support rapid chart updates. Full automation can streamline tasks such as the re-compilation of bathymetry and the migration from the imperial to the metric system for the OCS/MCD rescheme project. In addition, the time a fully automated solution would save could, indirectly, solve other compilation problems with allowing cartographers and HOs to steer their focus towards fixing them, such as the edge matching inconsistencies between adjacent ENCs. Towards this optimum goal, we present a research work 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. Toward the ultimate goal of full automation in chart compilation through scales, previous research efforts were investigated, and available nautical cartographic specifications were reviewed. The thereinto generalization guidelines were extracted, categorized and translated into rules to be defined in a constraints template as conditions to be respected during the generalization process. Accordingly, an automated nautical generalization (ANG) 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, and the largest scale chart data to perform the generalization to the target scale respecting topology constraints. Lastly, since safety is the ultimate product constraint in the domain, a custom validation tool detects any safety violation for the generalized linear features in the output database for user correction.

Full text

An Automated Nautical Chart Generalization Model
Nada Tamer1, Kastrisios Christos1, Calder Brian1, Ence Christie2
Greene Craig3, Bethell Amber3
1Center for Coastal and Ocean Mapping/UNH-NOAA Joint Hydrographic Center, University of New Hampshire, Durham, NH, USA
2 NOAA, Office of Coast Survey, Marine Chart Division, Silver Spring, MD, USA
3ESRI, Marine & Topographic Production Division, Redlands, CA, USA
tnada@ccom.unh.edu
Significant amounts of labor-intensive effort and time are needed for compiling and
maintaining Electronic Navigation Charts (ENCs). The great amount of data collected with
high-resolution systems that are being delivered to the charting divisions along with
initiatives within the divisions, such as the Office of Coast Survey (OCS)/ Marine Chart
Division’s (MCD) rescheme project, often lead to a bottleneck situation. Therefore,
nowadays, one of the main objectives in many Hydrographic Offices (HOs) is automating
generalization tasks to improve productivity in a cost-effective manner. However,
regardless of the various relevant research efforts and the advancements in technology,
most generalization tasks are still performed manually or semi-manually, where a
likelihood of human error is admitted. The ideal situation would be a fully automated
solution which could minimize the time and effort needed for ENC production and support
rapid chart updates. Full automation can streamline tasks such as the re-compilation of
bathymetry and the migration from the imperial to the metric system for the OCS/MCD
rescheme project. In addition, the time a fully automated solution would save could,
indirectly, solve other compilation problems with allowing cartographers and HOs to steer
their focus towards fixing them, such as the edge matching inconsistencies between
adjacent ENCs.
Towards this optimum goal, we present a research work 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. Toward the ultimate goal of full automation in chart compilation through
scales, previous research efforts were investigated, and available nautical cartographic
specifications were reviewed. The thereinto generalization guidelines were extracted,
categorized and translated into rules to be defined in a constraints template as conditions
to be respected during the generalization process. Accordingly, an automated nautical
generalization (ANG) 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, and the largest scale chart data to perform the generalization to the target scale
respecting topology constraints. Lastly, since safety is the ultimate product constraint in
the domain, a custom validation tool detects any safety violation for the generalized linear
features in the output database for user correction.