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Safety of navigation is essential for the global economy as maritime trade accounts for more than 80% of international trade. Carrying goods by ship is economically and environmentally efficient, however, a maritime accident can cause harm to the environment and local economies. To ensure safe passage, mariners tend to use already familiar routes as a best practice; most groundings occur when a vessel travels in unfamiliar territories or suddenly changes its route, e.g., due to extreme weather. In highly trafficked areas, the highest risk for ships is that of collision with other vessels in the area. In these situations, a network of previously traversed routes could help mariners make informed decisions for finding safe alternative routes to the destination, whereas a system that can predict the routes of nearby vessels would ease the burden for the mariner and alleviate the risk of collision. The goal of this project is to utilize Automatic Identification System data to create a network of “roads” to promote a route planning and prediction system for ships that makes finding optimal routes easier and allows mariners on the bridge and Autonomous Surface Vehicles to predict movement of ships to avoid collisions. This paper presents the first steps taken toward this goal, including data processing through the usage of Python libraries, database design and development utilizing PostgreSQL, density map generation and visualizations through our own developed libraries, an A* pathfinding algorithm implementation, and an early implementation of an Amazon Web Services deployment.

Measurements of the depth of the seabed vary widely in both horizontal and vertical accuracy. To convey this information to mariners, Zones of Confidence (ZOC) are defined for charts. A mosaic of ZOCs can be represented as a chart overlay. This study evaluates two novel designs for textures to represent ZOCs. Both use textures with countable elements to represent different ZOC levels. One uses a texture made of lines where the number of lines in a texture cell represents the confidence level; the other uses dot clusters where the number of dots similarly represents the ZOC level. In the study, these were compared with three alternatives that used color to respond and accuracy as dependent variables. The dot clusters design yielded the fastest responses overall. A method using levels of color transparency proved to be the slowest and least accurate., Evaluation and Representation, Colin Ware and Christos Kastrisios

Charted bathymetry encompasses the cartographer’s effort to accurately describe submarine topography based on the source information. A key factor in the selection process is that, at any location, the charted features do not give a false impression that the water depth is deeper than it actually is. Our research work focused on development of an automated solution for the identification of locations where this goal is violated. We present the research work from conceptualization to the development of an ArcGIS Toolbox named “Sounding Validation Toolbox” that consists of three automated tools, as well as the challenges and results in the process of making it operational with the Marine Chart Division of NOAA/Office of Coast Survey.

The Electronic Navigational Chart (ENC) consists of point, line, and area features compiled following the node-chain topological model. To ensure that the topological structure is valid, the International Hydrographic Organization (IHO) has developed a number of checks defined in Publication S-58. Many of the checks deal with the vertical component of the nautical chart with the aim to validate consistency among compiled geo-objects. Nevertheless, validation checks are not exhaustive and spatial relationships may be violated. The presented work identifies vertical discontinuities between depth areas and adjoining geo-objects in the ENC and following an iterative approach proposes fixes to the attributes and the geometry of the depth areas with errors.

Most navigational charts are an amalgamation of geospatial information of varying quality collected using different techniques at different times. Data collected with high resolution multi-beam echo sounders or lidar systems may co-exist on the chart with data collected with lead-line as far back as the 18th century. Data quality on charts is currently provided as a series of Category Zone of Confidence (CATZOC) values. For every part of the chart, CATZOC values provide information about the horizontal and vertical uncertainty of bathymetric information, as well as the seabed coverage and feature detection. CATZOC is portrayed in ECDIS as an additional layer with glyphs using a rating system of stars: six to two stars for the best to lowest quality data and “U” for unassessed data. With the provided information mariners may interpret the seabed morphology, identify shoals that pose a threat for the plotted voyage, and select routes that maintain under-keel clearance, however it is not always well understood and utilized by mariners. The aim of this research work is the development of new visualization and integration methods of data quality on charts to support decision making on board. In this two-fold work we begin with investigating new visualization methods for the bathymetric data uncertainty on charts and we subsequently utilize the results of the visualization work for the integration of marine navigation related information in support of decision making and safe navigation of ships.