Detection of discrepancies between nautical charts and new survey data using GIS techniques

ABSTRACT

Nautical charts are critical for safe navigation as long as they remain updated and trustworthy for the reality they depict. The increase in marine traffic and the growth of available data require that the process of assessing nautical chart adequacy, which consists of comparing information from a new survey with the one published in the ruling cartography, be both fast and effective. In this sense, this work aims to automate the detection of discrepancies between nautical charts and survey data to minimize human effort. We developed a Geographic Information System (GIS) location model based on specific rules derived from three analysis criteria: depth areas, minimum soundings, and bathymetric models. The model produces six outputs, two for each criterion, to support the ultimate human decision. We have tested the model in several hydrographic surveys, such as open waters and harbor surveys, and successfully validated it by comparing results with other available methods, such as current manual processes and Nautical Chart Adequacy Tools (CA Tools). Potential advantages over other methods are also evaluated and discussed, validating the usefulness of this novel approach for the adequacy and completeness evaluation of nautical charts. Our results deliver important benefits by enhancing the GIS techniques for nautical chart production and maintenance.

KEYWORDS:

• Chart maintenance
• electronic navigational chart
• hydrographic survey
• nautical chart
• safety of navigation

Acknowledgements

The authors would like to thank the institutional support provided by the NOVA Information Management School (NOVA IMS), and the Portuguese Hydrographic Institute (IH).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available in figshare at https://doi.org/10.6084/m9.figshare.19217520.v1

Additional information

Funding

This study was partially supported by the FCT (Fundação para a Ciência e a Tecnologia) under the projects PTDC/CTA-AMB/28438/2017 - ASEBIO and UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC).