Nautical Cartography

Research Projects

 
Hydrographic Sounding Selection 

For more information, see Task 31 in the 2021 JHC Progress Report

Modern hydrographic surveys collect vast amounts of high-resolution bathymetric data, which are generalized before delivery to nautical charting divisions for treatment and the final selection of soundings for charting. This process, known as Hydrographic Sounding Selection, involves reducing raw bathymetric data usually using a radius or grid-based thinning methods but practice has shown that these two result in an excessive number of soundings for the purpose while they do not guarantee safety. 

This project optimizes hydrographic sounding selection process, ensuring shoal-biased depth selection and legibility of soundings. For the intermediate product of hydrographic sounding selection, the project incorporates the sounding label footprint at scale and bathymetric data uncertainty, for the transition from high-resolution surveys to cartographic representations. 
 

Cartographic Sounding Selection

Link to Detailed Project Report

Nautical charts are relied upon to be accurate, efficient, and up to date, ensuring safe navigation for vessels worldwide. One of the most time-consuming tasks in chart production is cartographic sounding selection, which involves choosing spot depths that, along with other charted bathymetric features, best represent the seafloor’s features. The challenge lies in ensuring navigation safety while balancing clarity and completeness—retaining enough depth soundings for accurate navigation and avoiding clutter.

This project aims to develop an automated method for selecting cartographic soundings, reducing the manual workload and improving the efficiency of Electronic Navigational Chart (ENC) production. By retaining key soundings based on their significance to navigation, and integrating data quality and generalization principles in the selection process, this research effort ensures consistency of selections and faster, more reliable, and scalable ENC compilation, thus supporting modernized charting practices for safer global navigation.
 

Sounding Selection Verification Methods 

Link to Detailed Project Report

Depth soundings are critical to nautical charts, guiding vessels through safe passages while ensuring the accurate representation of underwater topography. Selecting soundings requires balancing accuracy, clarity, and safety. Traditional safety verification methods, such as the triangle test and edge test, help ensure that no shallow exist between chosen soundings, but these methods often yield excessive false alarms or fail to detect inconsistencies in complex bathymetric areas. Moreover, these traditional methods do not always align with how cartographers manually assess soundings in relation to, e.g., contours and terrain features.

This project aims to refine and automate the safety verification of selected soundings, ensuring they adhere to safety and cartographic standards. By improving selection consistency and reducing manual workload, the project enhances efficiency in hydrographic chart production.
 

Vertical Consistency Between Depth Areas and Adjacent Objects

For more information, see Task 37 in the 2019 JHC Progress Report

Electronic Navigational Charts (ENCs) are composed of spatial objects representing depth areas, land features, and other navigationally relevant geo-objects. These features must be topologically consistent, ensuring that transitions between depth areas and adjacent objects accurately reflect real-world bathymetry. However, current ENC production methods often introduce vertical discontinuities, where depth transition does not align correctly with shorelines, adjacent depth zones, or dredged areas. These inconsistencies complicate navigation, particularly for vessels using ECDIS-based under-keel clearance monitoring.

The Vertical Consistency project aims to identify and correct such inconsistencies by developing automated validation algorithms. By assessing the spatial and vertical relationships between adjacent ENC objects, it aims to ensure smooth and logical depth transitions and enhance the trustworthiness of ENC depth information, thus improving mariner safety and navigational efficiency. 
 

Depth Contours Generalization

Link to Detailed Project Report

Depth contours are vital to navigational charts, conveying the underwater terrain crucial for under-keel safety. Traditional line simplification approaches often sacrifice important features or fail to scale effectively across different navigational needs or result in contours that look unnatural. As a result, chart producers frequently rely on manual edits, which can be labor-intensive and prone to inaccuracies when working with dense, high-resolution data.

To address these shortcomings, this project investigates a contour simplification algorithm that emphasizes critical shallow-water details while reducing overall data volume. By selectively maintaining essential navigational features on the seaward side, the method aligns with core cartographic principles and ensures charts remain both accurate and efficient. In doing so, it offers a scalable solution that preserves safety-critical contours without overwhelming mariners with excess information.
 

Statistical Characterization of Depth Contours

For more information, see Task 37 in the 2017 JHC Progress Report

Automated depth contour generation from high-resolution bathymetric surveys is essential for modern Electronic Navigational Chart (ENC) production. However, raw contours are often noisy, visually unappealing, and difficult to interpret. To ensure cartographic effectiveness, contour generalization techniques should replicate the principles used by expert chart makers. Failing to do so may result in over-simplifying critical seabed features or retaining excessive detail, making contours feel unnatural and ENCs harder to use.

This project analyzes existing ENC data to identify statistical patterns in segment length, curvature, and density. By understanding these properties, this research effort aims to inform automated generalization algorithms to produce contours closer to those drawn by expert cartographers, improve ENC automation efforts, and ensure that automated contour generalization meets cartographic and real-world navigational needs.
 

Islands Generalization

Link to Detailed Project Report

Small islands and land features play an essential role in nautical navigation, acting as visual references, navigational hazards, and geographic landmarks. However, displaying land features in high detail is impractical, especially on smaller-scale charts, where unnecessary detail leads to clutter and inefficiency. On the other hand, elimination of land masses without considering dependent chart features can result in ENC errors if not navigational hazards. Traditional generalization techniques often remove islands based only on geometric criteria and islands’ spatial relationships, leading to a potential loss of important navigational features. This project develops a context-aware approach that preserves critical islands while simplifying or merging less significant ones, ensuring a clear and useful ENC at any scale.

This project establishes a hierarchical classification of islands, based on size, importance, and proximity to other chart features, and Implements a context-aware simplification strategy that considers depth contours, navigation routes, and aids to navigation. This project aims to provide a structured and intelligent approach to island generalization, ensuring that ENC remain clear and informative while minimizing unnecessary complexity to enhance chart usability while preserving safety-critical geographic features.
 

Collinear Vertices Removal  

This was reported in 2024 for the first time - link when on PressBooks !!!!!!!!

Electronic Navigational Charts (ENCs) contain vast amounts of vector data, including line and polygon geometries composed of thousands of vertices. While some level of detail is necessary, many of these vertices are redundant and contribute to large file sizes, slow rendering times, and inefficient data processing, particularly when deriving lower level of detail charts from larger scale charts. Removing unnecessary detail is currently a semi-automated process that causes frustration and often production software crashing. This project aims to reduce manual efforts with automatically removing collinear vertices while maintaining the topological and navigational integrity of adjoining and coincident ENC features. By optimizing ENC data storage, this research supports faster rendering, reduced memory requirements, and improved chart usability, making ENCs more efficient without sacrificing accuracy.
 

Depth Change Detection

Link to Detailed Project Report

Accurate and up-to-date bathymetry is crucial for safe navigation, especially in dynamic environments where the seafloor is subject to erosion, sediment deposition, dredging, and construction activities. New high resolution bathymetric data collection systems can rapidly survey such areas, however the increased speed that new surveys are delivered to charting organizations, combined with limited human resources, leads to a bottleneck situation where new data may wait for months before they are assigned to a cartographer for compilation.

This project leverages multi-temporal bathymetric datasets to detect and categorize bathymetric change with the aim to support hydrographic offices in responding to important seafloor changes faster, reducing navigational risks and improving maritime safety.
 

Data Quality Polygon Simplification

Link to Detailed Project Report

Electronic Navigational Charts (ENCs) data quality polygons (M_QUALs) are mandatory Electronic Navigational Chart (ENC) objects as they encode information about the Category Zone of Confidence (CATZOC) areas and the quality of bathymetric data on charts. Cartographers spend considerable time manually simplifying these polygons and ensure that the point density does not exceed 0.3mm at compilation scale to comply with IHO requirements.

This project focuses on automating the simplification of data quality polygons generated from gridded high-resolution surveys, with the aim to improve workflow efficiency and support faster, more scalable ENC production.
 

Towards Automated Compilation of ENCs

Link to Detailed Project Report

Nautical chart compilation is a highly manual and time-consuming process, requiring expert intervention for data selection, generalization, and symbology application. While automation has advanced in topographic mapping, the maritime domain lags behind due to the complexity of hydrographic data and safety constraints. Current Electronic Navigational Chart (ENC) production workflows have incorporated various semi-automated tools but they still require significant human input for cartographic decisions, such as feature selection, depth contour smoothing, and label placement.

This project aims to advance the automation of ENC production by developing rule-based models that translate cartographic principles into algorithmic processes. By leveraging constraint-based modeling, the research seeks to move towards automated chart generation from authoritative databases.

Visualization and Integration of Bathymetric Data Quality

Link to Detailed Project Report

Mariners rely on Electronic Navigational Charts (ENCs) for safe passage planning and execution with data quality uncertainty remaining a major challenge in decision-making. Accident reports confirm that failing to account for bathymetric data uncertainty can result in maritime accident and loss of life. A major concern has been the current ENC symbology as it does not effectively communicate bathymetric data reliability, leading to misinterpretations, missed hazards, and user confusion.

This project explores new methods to visualize bathymetric data quality into ECDIS to enhance mariner awareness and navigation safety. Through innovative symbology and usability testing, the project aims to ensure that mariners receive clear, actionable information about data reliability, enhancing voyage planning and navigational safety.

While visualizing bathymetric data quality is critical, an equally pressing challenge is how to integrate quality measures into ENC production and ECDIS route safety checks. CATZOC classifications should be updated to consider modern data reliability indicators, such as temporal variation, survey density, and data fusion uncertainties. This project explores new ways to encode and integrate data quality metrics into ENCs, leveraging advances in S-100 standards to support precision navigation and automated decision-making.

New S-1xx Symbology

Link to Detailed Project Report

Electronic Navigational Charts (ENCs) rely on standardized symbology to ensure that critical navigational information is effectively communicated to mariners. However, as new datasets emerge under the S-100 framework, there is a growing need to develop and refine symbology for next-generation products, such as S-122 (Marine Protected Areas), S-126 (Physical Environment), and S-131 (Marine Harbour Infrastructure).

This project focuses on developing new symbology for S-1xx Product Specifications, ensuring clarity, usability, and harmonization across different hydrographic products. A user-centered design approach is applied to ensure symbols align with mariner expectations and cognitive recognition patterns.

Roads of the Sea

Link to Detailed Project Report

Unlike land navigation, where fixed road networks guide movement, maritime navigation occurs in open waters, allowing vessels to take countless different routes between locations. While mariners follow commonly traveled paths, deviations due to weather, traffic, or emergencies significantly increase grounding and collision risks. Existing tracking technologies, such as the Automatic Radar Plotting Aid (ARPA) and Automatic Identification System (AIS), provide real-time monitoring but lack predictive capabilities to assist mariners and autonomous vessels in anticipating vessel movements and selecting safe alternative paths.

The Roads of the Sea (ROTS) project aims to map and predict frequently traveled sea routes using historical AIS data, supporting both human navigators and autonomous vessels in safe passage planning. By employing graph-based modeling for route identification, AI-driven trajectory prediction, and cloud-based processing, the project enhances route planning, mitigates collision risks, and advances autonomous navigation.
 

Nautical Surface Reconstruction and Interpolation Uncertainty

Link to Detailed Project Report

Bathymetric surfaces are fundamental for navigation, dredging, and environmental monitoring. However, hydrographic surveys often contain gaps due to survey limitations, outdated datasets, or sparse data collection. Traditional interpolation methods can introduce artifacts, distorting seabed representation and leading to navigational errors that distort the seabed representation, leading to navigational errors.

The Nautical Surface Reconstruction project aims to enhance bathymetric data completeness through data-driven interpolation techniques and machine-learning and to improve the accuracy and usability of bathymetric surfaces besides safety of navigation and ENCs. 
 

Survey-to-CATZOC

Link to Detailed Project Report

The Survey-to-CATZOC project focuses on ensuring the accurate representation of data quality in Electronic Navigational Charts (ENCs) by standardizing the process of allocating S-57 Category Zone of Confidence (CATZOC) / S-101 Quality of Bathymetric Data (QoBD) values from hydrographic surveys. While different hydrographic offices collect and process bathymetric data using varying methodologies, it is essential to establish a consistent approach to defining data reliability indicators, as these values influence safe route planning and voyage execution.

By developing guidelines for allocating CATZOC/QoBD values, this project aims to harmonize data quality representation across ENC producers, thus supports safer navigation with providing mariners with clear, standardized data quality indicators and preventing inconsistencies that could confuse them.

Data Quality S-1xx Cross-Check

Link to Detailed Project Report

As the S-100 framework expands, various S-1xx Product Specifications are being developed to support Electronic Navigational Charts (ENCs), bathymetric grids, water levels, marine traffic, and other hydrographic data layers. However, the data quality components of these specifications have been found to be inconsistent, leading to potential risks in data interpretation and usability.

The Data Quality S-1xx Cross-Check project aims to assess the alignment of S-1xx product specifications with IHO S-97 guidelines and / or propose changes to S-97 where necessary. This ensures that all S-1xx products maintain a harmonized approach to data quality representation, improving mariner confidence in ENC data reliability.
 

Collaboration with Maritime Training Centers

For more information, see Task 34 in the 2021 JHC Progress Report

Effective nautical charting relies not only on technological advancements but also on collaboration with the end users. This project fosters partnerships between hydrographic and  maritime professions, with the aim to ensure that nautical cartography aligns with mariner needs and modern navigation practices.

By engaging with maritime training centers and academies, the collaboration seeks to understand navigation challenges mariners face and improve chart usability through direct feedback and user participation in nautical charting developments such as new ENC symbology. It also aims to educate mariners on the principles of Electronic Navigational Chart (ENC) compilation and international charting standards, promoting safer and more efficient navigation.

Spatial Segmentation for Satellite-Derived Bathymetry (SDB)

For more information, see Task 24 in the 2022 JHC Progress Report

Satellite-Derived Bathymetry (SDB) has emerged as an alternative to traditional hydrographic surveys, particularly in remote or shallow regions where vessel-based surveys are costly and challenging. SDB techniques rely on multispectral satellite imagery to estimate water depth using algorithms that analyze light penetration and reflectance at different wavelengths.

Despite its potential, SDB adoption in nautical charting remains limited due to accuracy concerns and environmental dependencies, such as water clarity, seabed reflectance, and atmospheric conditions. This project investigates how multi-temporal and non-linear modeling approaches can improve SDB accuracy, particularly with the segmentation of the satellite imagery into smaller spatial units where water column and sea bottom characteristics are more uniform, making it more viable for hydrographic and navigational applications. 

Chart-Related Maritime Accident Causes

For more information, see Task 37 in the 2020 JHC Progress Report

Maritime accident reports contain information about chart-related issues related to maritime accidents including groundings and collisions. Extracting relevant information from these reports can provide essential insights but this process can be time consuming and challenging due to their complexity, narrative nature, and lack of standardized terminology.

This project aims to apply automated text analysis techniques to identify critical chart-related factors contributing to maritime accidents and explore methods to improve charting practices to mitigate chart-induced navigational risks.

Free and Open-Source Software for Ocean Mapping

For more information, see Task 34 in the 2022 JHC Progress Report

The ocean mapping community has long relied on commercial software for tasks such as hydrographic data processing, chart production, and quality control. While the availability of geospatial Free and Open-Source Software has grown significantly, offering alternatives that are cost-effective, adaptable, and community-driven, their adoption in hydrographic workflows remains limited. This is primarily due to Reliability concerns compared to commercial software, Lack of awareness about available open-source tools, and Limited documentation and user support.

While FOSSOM tools cannot yet fully replace proprietary software, they can play a valuable role in hydrographic data processing and visualization as they provide powerful supplementary capabilities, particularly in data quality control, 3D rendering, and geospatial analysis. Thereby, this project seeks to assess the potential of Free and Open-Source Software for use in Ocean Mapping (FOSSOM), evaluating its strengths, limitations, and suitability for charting, data visualization, and hydrographic data processing.

Industry Discovery

For more information, see Task 37 in the 2020 JHC Progress Report

Understanding the day-to-day challenges of ocean mapping professionals is critical for improving hydrographic data processing, cartographic workflows, and product usability. However, these challenges vary widely across the hydrographic workflow, including data collection, processing, chart compilation, research, production management, and end-user interaction. This project aims to systematically assess industry needs, providing insights that inform research priorities and the development of new tools. Through extensive industry interviews we gather valuable insights into the operational needs of ocean mapping professionals from hydrographic offices, private industry, and academic institutions. This research helps align technological advancements with real-world demands, ensuring that hydrographic data processing and chart production remain efficient, reliable, and aligned with end-user requirements.