Abstract. Advancements in geospatial technology have benefited the hydrographic and maritime professions in many ways. Yet, compared to hydrographic data collection and processing, chart compilation workflows remain relatively slow, mainly due to limited human resources and the availability of automated algorithms that respect nautical charting constraints and Electronic Navigational Chart (ENC) database requirements. This work presents our research efforts to streamline the nautical chart compilation process through the introduction of automated processes and improving the efficiency and accuracy of existing. Among these processes are fundamental generalization tasks such as those for soundings, islands, and depth contours; ENC product specific requirements, such as those for reducing file size through the removal of collinear vertices forming polylines and polygons; and the updating of dependent features in the ENC database after generalization of one of their shared geometries.

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 testbeds 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.

Although much progress has been made in recent years to fully map the world ocean, only approximately 20% is adequately mapped to modern standards. Filling in the remainder must by necessity be a multimodal effort, with traditional ocean mapping technologies such as crewed survey ships with multibeam echosounders being mixed with newer systems such as uncrewed, sail-powered mapping systems. Volunteer data from any ship with an echosounder can also be used, but although there have been commercial efforts in this field, most of these systems do not contribute data into the public arena and public entities have largely avoided this field due to the complexities of costs, data processing, and uncertainty on how to handle the effort. This article describes the design of an end-to-end system for managed volunteer bathymetric collection consisting of an inexpensive (~ $\$ $ 20) wireless “Ocean of Things” data logger for NMEA0183 and NMEA2000 data, associated firmware to manage the collection, a mobile device application to offload, aggregate, and transfer the data into the cloud, and a cloud segment to process the data and submit it to an international data repository. All of the design has been released under an Open Hardware or Open Source license, allowing independent organizations to initiate data collection efforts without having to do any of the design work themselves. The goal is to provide a simple, approachable implementation, encouraging greater adoption of these ideas in hard-to-reach areas of the world with minimal effort on the part of the host organization.

When planning for ship navigation or compiling data for a bathymetry map, the navigator or mapper uses many different sources of bathymetry information and navigation hazards. The quality of these sources is inconsistent in general, however, making it challenging to provide a coherent picture for planning. Here, we describe an approach for consistent planning/mapping that uses a combination of soft computing and Bayesian estimation. The case study used to exercise this system involves NOAA Electronic Nautical Charts for an area in the Chesapeake Bay. We first interpolate each set of irregularly spaced soundings to gridded versions of each point-cloud set. Each of these intermediate grids is then aggregated into a fused bathymetric realization using order weighted averaging (OWA) to provide the weights for each source based on their subjective reliabilities. The OWA allows for fusion informed by the user's subjective risk allowed in the reconstruction of the seafloor surface and provides quantitative methods to generate, use, and record subjective reliability weights. Each sounding point that went into the bathymetry estimate is then categorized as "no-go," "caution," or "go" status. Reliability estimates are reused for weighted Bayesian categorization of each output grid cell to compute the navigable surface.

Abstract. The compilation of Electronic Navigational Charts (ENCs) requires significant amount of time, labor-intensive efforts, and cost. Despite the advancements in technology and the various research efforts, generalization tasks are still performed manually or semi-manually with expected human errors. The dramatic increase in the amount of data that is collected by modern acquisition systems, in addition to the increasing timeline expected by the end-users, are constantly driving Hydrographic Offices (HOs) toward the investigation and adoption of more advanced and effective ways for automating the generalization tasks to speed up the process, minimize the cost, and improve productivity. Full automation of the nautical chart compilation process has been unreachable due to the strict nautical cartographic constraints (and particularly those of safety and topology) that pose a challenge for most of the available generalization tools, while it remains questionable whether automation can replace human thought processes. In this paper, we discuss a research effort for an Automated Nautical-chart Generalization (ANG) model in the Esri environment. The ANG model builds upon the nautical chart generalization guidelines and practice and utilizes available tools in the Esri environment to perform the generalization of selected ENC features to the target scale. Safety constraints in the marine domain is of utmost importance, however, since most of the readily available tools do not respect safety, the main goal of this effort has been an output with no topological violations. In the current phase of the project, we evaluate safety of soundings and contour for user fixing and while the validation of bathymetry is a well-researched topic, there was the need for an automated process to identify the sections of the generalized contours that have been displaced toward the shallow water side Therefore, this work also presents a safety validation tool that detects the contours’ safety violations in the output. The tool is composed of three main stages that run individually after the ANG model is complete with the aim to highlight the safety violations for fixing by cartographers.

Conventional techniques for extracting bathymetric soundings from LiDAR point clouds are at best semi-automated and require considerable manual effort. An algorithm that couples a widely used sonar data processing method with a newly developed machine-learning(ML)-based algorithm was evaluated for accuracy and potential operationalisation. Data representing an operationally realistic range of environmental and data conditions comprised 103 500 m-by-500 m data tiles for method development/calibration and 20 tiles for validation located in the Florida Keys. Tiles are processed individually to classify each LiDAR pulse return (“sounding” in hydrographic terminology) as bathymetry or not. Compared to a reference classification an average agreement of about 90% was produced for the calibration and validation data sets, and accuracy varied depending on ocean bottom and data conditions. The average false negative rate – the most important metric in hydrographic mapping – was about 5%. Processing time for tiles containing the average number of soundings (seven million) on a desktop computer was approximately 100 min. The algorithm does not require in situ ground-“truth” data for training or calibration, although its adaptation to other geographic and data conditions might require data-guided adjustment of ML tuning parameters.

This study describes the geomorphometries of archipelagic aprons on the southern flanks of the French Frigate Shoals and Necker Island edifices on the central Northwest Hawaiian Ridge that are hotspot volcanoes that have been dormant for 10-11 m.y. The archipelagic aprons are related to erosional headwall scarps and gullies on landslide surfaces but also include downslope gravitational features that include slides, debris avalanches, bedform fields, and outrunners. Some outrunners are located 85 km out onto the deep seafloor in water depths of 4900 m. The bedforms are interpreted to be the result of slow downslope sediment creep rather than products of turbidity currents. The archipelagic aprons appear to differ in origin from those off the Hawaiian Islands. The landslides off the Hawaiian Islands occurred because of oversteepening and loading during the constructive phase of the islands whereas the landslides off the French Frigate Shoals and Necker Island edifices may have resulted from vertical tectonics due to the uplift and relaxation of a peripheral bulge or isolated earthquakes long after the edifices passed beyond the hotspot. The lack of pelagic drape in water depths above the 4600 m depth of the local carbonate compensation depth suggests that the archipelagic apron off the French Frigate Shoals edifice is much younger, perhaps Quaternary in age, than that off the Necker Island edifice, which has a 50 m pelagic drape. The pelagic drape off the Necker Island edifice suggests that the landslides may be as old as 9 Ma. The lack of pelagic drape off the French Frigate Shoals edifice suggests that the most recent landslides are more recent, perhaps even Quaternary in age. The presence of a chute-like feature on the mid-flank of the French Frigate Shoals edifice appears to be the result of rejuvenated volcanism that occurred long after the initial volcanism ceased to build the edifice.

Shallow-water depth estimates from airborne lidar data might be improved by using sounding attribute data (SAD) and ocean geomorphometry derived from lidar soundings. Moreover, an accurate derivation of geomorphometry would be beneficial to other applications. The SAD examined here included routinely collected variables such as sounding intensity and fore/aft scan direction. Ocean-floor geomorphometry was described by slope, orientation, and pulse orthogonality that were derived from the depth estimates of bathymetry soundings using spatial extrapolation and interpolation. Four data case studies (CSs) located near Key West, Florida (United States) were the testbed for this study. To identify bathymetry soundings in lidar point clouds, extreme gradient boosting (XGB) models were fitted for all seven possible combinations of three variable suites—SAD, derived geomorphometry, and sounding depth. R2 values for the best models were between 0.6 and 0.99, and global accuracy values were between 85% and 95%. Lidar depth alone had the strongest relationship to bathymetry for all but the shallowest CS, but the SAD provided demonstrable model improvements for all CSs. The derived geomorphometry variables contained little bathymetric information. Whereas the SAD showed promise for improving the extraction of bathymetry from lidar point clouds, the derived geomorphometry variables do not appear to describe geomorphometry well.

The goal of this work was to evaluate if routinely collected but seldom used airborne lidar metadata - 'point attribute data' (PAD) - analyzed using machine learning/artificial intelligence can improve extraction of shallow-water (less than 20 m) bathymetry from lidar point clouds. Extreme gradient boosting (XGB) models relating PAD to an existing bathymetry/not bathymetry classification were fitted and evaluated for four areas near the Florida Keys. The PAD examined include 'pulse specific' information such as the return intensity and PAD describing flight path consistency. The R-2 values for the XGB models were between 0.34 and 0.74. Global classification accuracies were above 80% although this reflected a sometimes extreme Bathy/NotBathy imbalance that inflated global accuracy. This imbalance was mitigated by employing a probability decision threshold (PDT) that equalizes the true positive (Bathy) and true negative (NotBathy) rates. It was concluded that 1) the strength of the bathymetric signal in the PAD should be sufficient to increase accuracy of density-based lidar point cloud bathymetry extraction methods and 2) ML can successfully model the relationship between the PAD and the Bathy/NotBathy classification. A method is also presented to examine the spatial and feature-space distribution of errors that will facilitate quality assurance and continuous improvement.

To automate extraction of bathymetric soundings from lidar point clouds, two machine learning (ML1) techniques were combined with a more conventional density-based algorithm. The study area was four data "tiles" near the Florida Keys. The density-based algorithm determined the most likely depth (MLD) for a grid of "estimation nodes" (ENs). Unsupervised k-means clustering determined which EN's MLD depth and associated soundings represented ocean depth rather than ocean surface or noise to produce a preliminary classification. An extreme gradient boosting (XGB) model was fitted to pulse return metadata - e.g. return intensity, incidence angle - to produce a final Bathy/NotBathy classification. Compared to an operationally produced reference classification, the XGB model increased global accuracy and decreased the false negative rate (FNR) - i.e. undetected bathymetry - that are most important for nautical navigation for all but one tile. Agreement between the final XGB and operational reference classifications ranged from 0.84 to 0.999. Imbalance between Bathy and NotBathy was addressed using a probability decision threshold that equalizes the FNR and the true positive rate (TPR). Two methods are presented for visually evaluating differences between the two classifications spatially and in feature-space.

Knowledge of offset vectors from vessel mounted sonars, to systems such as Inertial Measurement Units and Global Navigation Satellite Systems is crucial for accurate ocean mapping applications. Traditional survey methods, such as employing laser scanners or total stations, are used to determine professional vessel offset distances reliably. However, for vessels of opportunity that are collecting volunteer bathymetric data, it is beneficial to consider survey methods that may be less time consuming, less expensive, or which do not involve bringing the vessel into a dry dock. Thus, this article explores two alternative methods that meet this criterion for horizontally calibrating vessels. Unmanned Aircraft Systems (UASs) were used to horizontally calibrate a vessel with both Structure from Motion photogrammetry and aerial lidar while the vessel was moored to a floating dock. Estimates of the horizontal deviations from ground truth, were obtained by comparing the horizontal distances between targets on a vessel, acquired by the UAS methods, to multiple ground truth sources: a survey-grade terrestrial laser scan and fiberglass tape measurements. The investigated methods were able to achieve horizontal deviations on the order of centimeters with the use of Ground Control Points.

Marine Volunteered Geographic Information (informally "crowdsourced bathymetry") has raised much interest within the authoritative hydrographic community as a means to cheaply gather information to satisfy chart updating requirements. So far, however, a routine path to the official chart has been rare, mainly due to lack of calibration and other metadata that would satisfy liability concerns. As an alternative to these ideas, a data collection system is proposed which, by design, can auto-calibrate and provide other data quality guarantees, and thereby generate data that by construction should be qualified for hydrographic use. This idea is termed here Trusted Community Bathymetry (TCB). A design for such a system is outlined, and its performance demonstrated experimentally through a prototype system based on a low-cost, post-processed GNSS receiver and NMEA data logger. By comparison against NGS control and survey-grade GNSS equipment, it is shown that the TCB system achieves centimeter to decimeter-level positioning in 3D, auto-calibrates vertical offsets to the sonar transducer within a decimeter, and provides realtime uncertainty estimates for ellipsoid-referenced soundings. Additionally, in an underway field trial, the total vertical uncertainty of the soundings is shown to be within the limits required for IHO Order 1b (S.44, 5 Ed.) surveys.

The Electronic Navigational Chart (ENC) consists of point, line, and area features compiled following the nodechain 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 geoobjects. 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. Keywords— ENC validation checks; automated nautical cartography; digital terrain modelling; topographic surface; nautical surface; surface reconstruction;

The processes controlling advance and retreat of outlet glaciers in fjords draining the Greenland Ice Sheet remain poorly known, undermining assessments of their dynamics and associated sea-level rise in a warming climate. Mass loss of the Greenland Ice Sheet has increased six-fold over the last four decades, with discharge and melt from outlet glaciers comprising key components of this loss. Here we acquired oceanographic data and multibeam bathymetry in the previously uncharted Sherard Osborn Fjord in northwest Greenland where Ryder Glacier drains into the Arctic Ocean. Our data show that warmer subsurface water of Atlantic origin enters the fjord, but Ryder Glacier’s floating tongue at its present location is partly protected from the inflow by a bathymetric sill located in the innermost fjord. This reduces under-ice melting of the glacier, providing insight into Ryder Glacier’s dynamics and its vulnerability to inflow of Atlantic warmer water.

Navigational charts contain a combination of geospatial information of varying quality collected at different times using various techniques. Bathymetric data quality is mainly encoded in electronic charts with the Category of Zones of Confidence (CATZOC). CATZOC provides information about the horizontal and vertical uncertainty of depth information, as well as the seabed coverage and feature detection. It is visualized in Electronic Chart Display and Information Systems (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. The current symbology creates visual clutter which is worse in areas of high quality bathymetry. Furthermore, horizontal and vertical uncertainties may not be adequately assessed by the user. This paper presents a research program aimed at the development of a method for portraying bathymetric data quality and for integrating the quantified uncertainties in ECDIS.

More than 844,000 km(2) of the northern Line Islands Ridge mapped with multibeam bathymetry and backscatter provide unprecedented views of the geomorphology of this isolated area in the central equatorial Pacific Ocean. A compilation of all available multibeam data in the area reveals six extensive submarine dendritic channel systems that encompass a combined drainage area that exceeds 60,000 km(2). The channel systems occur in a predominately carbonate environment and are the longest calciclastic submarine channel systems mapped in the oceans to date. The channel systems occur in a carbonate-dominated region well above the carbonate compensation depth and have developed into the surface of basins that are surrounded by small guyots and seamounts that make up a discontinuous rim around the summit of the northern Line Island Ridge. The channels have mostly straight or gently curved well-developed tributaries and main reaches. Although the Line Island Ridge has been dated at 86 to 68 Ma old, the channels occur on the surface and are not buried by any significant sediment accumulations. Levees are very rare along the channel banks and no bathymetric expression of submarine fans was found where the channels exit onto the adjacent abyssal basins. There is sparse evidence of landslide deposits throughout the ridge although the flanks of the guyots exhibit numerous headwall scarps. The presence of plunge pools below the northwest escarpment, together with well-defined channels meters to hundreds of meters deep relative to the surrounding seafloor, suggests the channels might be relatively recent (perhaps late Neogene or even younger) features developed long after the ridge subsided more than a kilometer below sea level.

Depth areas are utilized by the Electronic Chart Display and Information Systems (ECDIS) along with the vessel’s characteristics (e.g., draft, squat) and other situational information (e.g., tides) for separating safe areas from those unsafe to navigate. Any error in their compilation is carried over to the analysis performed in the ECDIS. As a result, waters may be portrayed deeper, thus posing a risk to the vessel navigating them, or may appear shoaler, thus triggering useless ECDIS alarms which contribute to the situation known as “mariner’s deafness”. With the exception of crisp boundaries where abrupt changes are expected, the transition between depth areas should be smooth and continuous. In this paper we present a research toward a mechanism for identifying discontinuities and an error remediation approach that proposes changes to the encoded depth range and the geometry of depth areas with identified discontinuities, for the cartographer’s attention.

A method for partitioning a large computation task (direct, variable resolution bathymetric grid construction from raw observations) into thread-parallel code is described. Based on the data density estimated for the first pass of the chrt algorithm, this algorithm statically partitions the estimation task into spatially distinct blocks of approximately equal total data observation count so that each can be executed in parallel and be expected to complete approximately concurrently. No communication between blocks or further load balancing is therefore required. A branch-and-bound algorithm is used to control the complexity of the partitioning task, but the computation time increases significantly as more partitions are required, leading to a degree of diminishing returns for allocating further computational resources and suggesting alternative approaches for high thread-count systems. Speed-up of the algorithm over a pair of test datasets (using real-world hydrographic survey data) shows that the performance consistently improves with the number of computational tasks assigned, initially (super-) linearly, although ultimately sub-linearly as other resource sharing limitations take over. An overall speedup of 4.1 times is demonstrated with a quad-core single-processor workstation.

Density-based approaches to extract bathymetry from airborne lidar point clouds generally rely on histogram/frequency-based disambiguation rules to separate noise from signal. The present work targets the improvement of such disambiguation rules by enhancing each pulse with a machine learning-based estimate of its p(Bathy) - i.e., its probability of truly being bathymetry. Extreme gradient boosting (XGB) is used to assess the strength of bathymetric signal in pulse return metadata. Because lidar point clouds can be highly imbalanced between Bathymetry and NotBathymetry, three strategies for mitigating the effects of imbalanced samples were examined. Impacts of an imbalanced lidar point cloud were successfully mitigated by: · Applying an “optimal” decision threshold (ODT) that equalizes accuracy for Bathymetry and NotBathymetry to p(Bathy) rather than using a conventional probability decision threshold (PDT) of 0.50, and · Using proportional class weighting to fit the XGB model. However, decomposing a confusion matrix by iteratively discarding misclassified points and re-fitting an XGB model was not successful in improving the strength or detectability of the bathymetric signal in the metadata. The same was true for iteratively discarding correctly classified points. The bathymetric signal in the metadata was found to be sufficiently strong to explore the operational incorporation of results into the disambiguation rules of density-based bathymertric extraction methods.