Crowd-Sourced Bathymetry (CSB) has gained recognition in Canada and internationally as a data source in uncharted and inadequately charted environments. However, the acquired CSB data is challenged with credibility and chartability issues, which are a result of the provenance of the crowd data, therefore the “total cost of ownership” for the data – meaning the cost in time and effort to qualify the data for use – will remain high. This research focuses on improving the uncertainty and reliability of the vertical offset estimate in CSB, thereby improving the chartability of the acquired data. CSB data on vessels of opportunity often lacks a surveyed vertical offset between the Global Navigation Satellite System (GNSS) antenna and the sonar. This unknown, or poorly known, offset tremendously impacts the Total Vertical Uncertainty (TVU) of the resulting CSB data. This research aims to estimate the unknown offsets of the vertical components using a Recursive Least Squares (RLS) solution which updates over time. The method is applied as the vessel travels over modern survey data where the chart datum depth, chart datum to ellipsoid separation, GNSS ellipsoidal height, predicted or observed water level, and measured echosounder depth below the transducer are known. These observations and their uncertainty estimates are used to solve for the unknown parameters of the transducer draft and GNSS antenna to transducer offset using RLS. Test data from the Gulf of Saint Lawrence was applied to the algorithm, and the consequences of applying different source data to the weighted recursive adjustment, including ocean model-derived predicted tides were explored. The system improved the quality of CSB data and could be broadly applied to contribute worldwide to the CSB data collection efforts.

Mariners have interest in contributing Volunteer Bathymetric Information (VBI; a.k.a. Crowdsourced Bathymetry). However, contributing VBI to entities such as IHO DCDB (e.g., to benefit Seabed 2030) can be difficult due to lack of time and data management expertise. Trusted Nodes (TNs) are intended as points of aggregation that take this burden away from mariners, submitting VBI on their behalf. However, TNs face the challenge of building and maintaining data-processing pipelines that produce data conforming to standardized schema, such as IHO B-12. Given the standardization of products, it is reasonable to adopt a standardized community data management platform that enables TNs to collect and own their data. The Wireless Inexpensive Bathymetry Logger (WIBL) platform provides such a standardized data management software platform. WIBL data management (WIBL-DM) provides modular, extensible, and open-source software tools for managing the production of VBI across data management lifecycle phases. This work describes the design and application of WIBL-DM tools to provide a “Trusted Node in-a-box” experience to accelerate the establishment of TNs. WIBL-DM tools are composed of Python and C++ programs that can be used locally or in cloud-computing architectures. WIBL-DM can process native WIBL logger data or convert data from proprietary loggers. Example PowerShell scripts are provided to enable local batch processing. When deployed in serverless cloud environments (using provided infrastructure-as-code scripts), WIBL-DM enables hands-free data processing without requiring cloud-computing expertise. WIBL-DM cloud processing tools are modular and loosely coupled via messaging systems. The WIBL-DM cloud architecture can therefore serve as a basis for developing Software as a Service (SaaS) TN platforms to support custom workflows suitable to different TNs using a single cloud system. Such SaaS platforms would enable “virtual TN-for-rent” infrastructure for those that do not want to run their own data processing tools, while still maintaining ownership and control over their data.

According to the 2023 Gap-Analysis report by the Canadian Hydrographic Service (CHS), over 85% of Canada's Exclusive Economic Zone (EEZ) has yet to be surveyed using modern bathymetric methods. Crowdsourced Bathymetry (CSB) data, which consists of depth measurements from vessels, collected using standard navigation instruments, while engaged in routine maritime operations, represents an increasingly valuable source of data for filling these gaps. CSB data is accumulating in databases such as the International Hydrographic Organization's (IHO) Data Centre for Digital Bathymetry (DCDB) and CHS is currently developing a dataflow to facilitate its utilization for both navigational and non-navigational purposes in Canada. In this presentation, we will provide an overview of CHS' strategy for automating the processing, validation, and integration of CSB data hosted in the IHO DCDB. Moreover, we will delve into our water reduction approach for reducing CSB data to chart datum and the methods we are exploring to evaluate the accuracy of depth information in CSB data. The primary objective of this presentation is to provide an update on CHS' progress in developing its CSB dataflow, and to share insights with the hydrographic community with the goal of globally enhancing access to and utilization of CSB data.

The term ghost gear refers to abandoned, lost, or discarded fishing gear. Hurricane Fiona, which occurred in 2022, had severe impacts on numerous Newfoundland and Labrador fishing outports, leading to harvester's gear being dragged and swept to sea. The Department of Fisheries and Oceans Canada (DFO) provided funding to the Canadian Center for Fisheries Innovation (CCFI) to assist in identifying and retrieving ghost gear to reduce the amount of microplastics entering the ocean. Utilizing a side scan sonar, a remotely operated vehicle (ROV), and drop cameras helped in identifying the gear, while methods such as dragging, grappling, and hand-picking were used for retrieval. The main objectives for completing this project included the identification and retrieval of ghost gear from the water and the surrounding coastlines and the presentation of the results. Methods used in completing these objectives involved collecting side scan sonar data and drop camera footage to identify any gear on the seafloor, processing data to support accurate retrieval efforts, and formatting all results through interactive maps. By utilizing maps to effectively showcase data, the results obtained from this project accurately present all information for each area where identification and retrieval efforts were conducted. An example of one of the resulting maps is shown in Figure 1 below.