Conference PaperPDF Available

The Nautical Cartographic Constraints and an Automated Generalization Model

Authors:
Tamer Nada at University of New Hampshire
Tamer Nada
Christos Kastrisios at University of New Hampshire
Christos Kastrisios
Calder Brian
Calder Brian
Calder Brian
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Ence Christie
Ence Christie
Ence Christie
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Show all 7 authorsHide
Download full-text PDF
Read full-text
Download citation

Abstract

Current methods for nautical charts generalization are strongly human interactive and time-consuming. Significant amounts of effort are needed for generalizing, compiling, updating, maintaining, and consistency checking those products. The ideal situation would be a fully automated solution for generating products on demand, at the right scale, at the point of use, and directly from the seamless database, that respect all application specific constraints (i.e., Safety, Legibility, Topology, and Morphology). Such a solution would solve many related problems, minimize the time and effort needed for ENC production and support rapid update. However, regardless of the advancements in technology and the various research efforts, generalization tasks for nautical chart compilation are still performed mostly manually or semi-manually, where a likelihood of human error must be admitted. Furthermore, practice has shown that there is often a trade off among the various generalization constraints as fixing one violation may result in the violation of another constraint. Towards this optimum goal, we are conducting a project that aims to investigate the previous efforts for automated map production, review the available relevant nautical cartographic standards and specifications, and extract and categorize the nautical chart generalization guidelines thereinto. These are subsequently translated into rules and defined in a template as conditions to be respected during the generalization process. According to a hierarchy level, a cost will be related to any violation according to its importance to the safety of navigation. Since fully satisfying all constraints seems infeasible, the optimal generalized chart should be the one with the lowest cost. For the implementation, a multi-agent generalization model is under development in the ESRI nautical environment that will utilize the template and the source data within the areas of interest to perform the generalization for the target scale (Figure). Figure: The concept of the nautical generalization model
Content uploaded by Tamer Nada
Author content
All content in this area was uploaded by Tamer Nada on Jun 09, 2022
Content may be subject to copyright.
The Nautical Cartographic Constraints and an Automated Generalization Model
Nada Tamer1, Kastrisios Christos1, Calder Brian1, Ence Christie2
Greene Craig3, Bethell Amber3, Hosuru Madhu3
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
Current methods for nautical charts generalization are strongly human interactive and time-consuming.
Significant amounts of effort are needed for generalizing, compiling, updating, maintaining, and consistency
checking those products. The ideal situation would be a fully automated solution for generating products on
demand, at the right scale, at the point of use, and directly from the seamless database, that respect all
application specific constraints (i.e., Safety, Legibility, Topology, and Morphology). Such a solution would
solve many related problems, minimize the time and effort needed for ENC production and support rapid
update. However, regardless of the advancements in technology and the various research efforts,
generalization tasks for nautical chart compilation are still performed mostly manually or semi-manually,
where a likelihood of human error must be admitted. Furthermore, practice has shown that there is often a
trade off among the various generalization constraints as fixing one violation may result in the violation of
another constraint.
Towards this optimum goal, we are conducting a project that aims to investigate the previous efforts for
automated map production, review the available relevant nautical cartographic standards and specifications,
and extract and categorize the nautical chart generalization guidelines thereinto. These are subsequently
translated into rules and defined in a template as conditions to be respected during the generalization process.
According to a hierarchy level, a cost will be related to any violation according to its importance to the safety
of navigation. Since fully satisfying all constraints seems infeasible, the optimal generalized chart should be
the one with the lowest cost. For the implementation, a multi-agent generalization model is under
development in the ESRI nautical environment that will utilize the template and the source data within the
areas of interest to perform the generalization for the target scale (Figure).
Figure: The concept of the nautical generalization model
... When the relevant chart features and/or the cartographic model (e.g., sounding label size and dimensions) are not considered by the automation algorithm, the adjustments that the cartographers must make may lead to a considerably different sounding selection, consequently, reducing their trust on the tool. Socha and Stoter [33] investigated the automated generalization of nine ENC feature classes, their work being the first step toward fully automated nautical chart compilation, Additionally, the developing work by Nada et al. [34,35] aims to extract, categorize, and translate nautical chart generalization guidelines into machine-readable conditions to be respected by a series of discrete generalization tasks in a chart compilation model in the ESRI ArcGIS environment. This effort is expected to add a building block to the automation efforts in nautical cartography and to serve as the foundation of future research efforts. ...
... With the aim to contribute to the efforts for expanding the holistic automated generalization approaches to the maritime domain, reduce the compilation time of fundamental chart features (land areas, depth areas and contours, soundings, buildings) and develop ENC-like products for applications beyond nautical charting, this work reviews available data sources and investigates the integration, testing, and improvement of existing generalization approaches. The work builds upon the professional experience of authors with nautical charting workflows and their research efforts to automate data collection [47] and individual data generalization tasks [15,32], to validate chart data requirements [6,35,[48][49][50][51][52], to model the nautical chart compilation workflow and generalize ENC Skin-of-the-Earth features with no topological errors [34], to shed light and gain knowledge on the capabilities of free and open software for use in ocean mapping workflows [53,54], to build innovative chart symbology [8,46,[55][56][57][58] and custom chart web services [45]. ...
Increasing Efficiency of Nautical Chart Production and Accessibility to Marine Environment Data through an Open-Science Compilation Workflow
Article
Full-text available
  • Mar 2023
  • ISPRS
Electronic Navigational Chart (ENC) data are essential for safe maritime navigation and have multiple other uses in a wide range of enterprises. Charts are relied upon to be as accurate and as up-to-date as possible by the vessels moving vast amounts of products to global ports each year. However, cartographic generalization processes for updating and creating ENCs are complex and time-consuming. Increasing the efficiency of the chart production workflow has been long sought by the nautical charting community. Toward this effort, approaches must consider intended scale, data quality, various chart features, and perform consistently in different scenarios. Additionally, supporting open-science initiatives through standardized open-source workflows will increase marine data accessibility for other disciplines. Therefore, this paper reviews, improves, and integrates available open-source software, and develops new custom generalization tools, for the semi-automated processing of land and hydrographic features per nautical charting specifications. The ro-bustness of this approach is demonstrated in two areas of very different geographic configurations and the effectiveness for use in nautical charting was confirmed by winning the first prize in an international competition. The presented rapid data processing combined with the ENC portrayal of results as a web-service provides new opportunities for applications such as the development of base-maps for marine spatial data infrastructures.
View
... An abstract schematic description of the automated nautical generalization model(Nada et al. 2022). ...
Towards automating the nautical chart generalization workflow
Article
Full-text available
  • Jun 2024
Current nautical chart generalization methods are notably labor intensive, requiring significant levels of human intervention to compile, update, and maintain chart products. The ideal situation would be a fully automated solution for generating nautical charts seamlessly from a comprehensive database, on demand, at the appropriate scale, at the point of use, and respecting the product constraints. However, regardless of the various research efforts and advancements in technology, including those involving AI, nautical chart generalization tasks are still performed manually, or semi-manually, where a likelihood of human error is expected. This manuscript presents a research effort toward automated chart compilation through scales. Nautical chart generalization guidelines are extracted, categorized, and translated into machine readable rules, utilized by a multi-agent model to perform the generalization of the source data to the target scale with no topological violations. This is illustrated in three test beds for the most important ENC feature classes. While topology is maintained, the model utilizes readily available algorithms that, generally, compromise safety. Therefore, a custom validation tool detects safety violations for user intervention. The model has been made flexible to incorporate algorithms that align with application constraints, especially safety, as they become available.
View
ResearchGate has not been able to resolve any references for this publication.