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Abstract
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.
Publication Date
6-19-2024
Journal Title
Geo-spatial Information Science
Rights
© 2024 Wuhan University
Publisher
Taylor & Francis
Digital Object Identifier (DOI)
Document Type
Article
Recommended Citation
Nada, T., Kastrisios, C., Calder, B., Ence, C., Greene, C., & Bethell, A. (2024). Towards automating the nautical chart generalization workflow. Geo-Spatial Information Science, 1–26. https://doi.org/10.1080/10095020.2024.2366873
Comments
This is an open access article published by Taylor & Francis in Geo-spatial Information Science in 2024, available online: https://dx.doi.org/10.1080/10095020.2024.2366873