Amber Bethell’s scientific contributions

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.

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.