Modern Methods Adopted by Canadian Hydrographic Service for Bathymetry Compilation
The Chart Hydrographic Office (CHS) has embarked on an innovative journey to optimize its ENC production and updating workflows, leveraging automated software functions to streamline cartographic workflows and deliver a safer and more efficient navigational product.
The CHS's automated workflow for creating ENC-ready bathymetric features involves two primary components: a contouring workflow and a sounding selection workflow. In the contouring workflow, point cloud preparation, coastline creation and generalization, and depth contours creation and generalization are carried out. The sounding selection workflow, on the other hand, includes a point cloud surface preparation step, followed by the calculation of the density of significant and background soundings.
The workflow has been proven relevant with multiple seabed types and has significantly cut the level of effort required to create new ENCs. Existing features with sounding values are converted to a point cloud, and unnecessary model selection is avoided in their vicinity. A second iteration is used to select a few soundings that may have become unsafely covered by the selection of the background ones.
However, the CHS is seeking further optimizations in this process. One key area of focus is the quality of bathymetric data input. Ensuring accurate and high-resolution bathymetric data is essential. This can involve merging multiple data sources, such as local high-resolution surveys combined with global datasets, to capture fine details reliably.
Advanced numerical modeling and dynamic downscaling are also integrated to refine feature accuracy. Such models that adjust flood depths and water levels dynamically based on bathymetric input improve flood risk assessments and, by extension, the reliability of bathymetric data for navigation purposes.
Automated data correction and filtering workflows are incorporated to ensure more consistent and reliable depth measurements. Algorithmic corrections for known biases, such as transducer spacing and vessel squat effects, are applied to improve the accuracy of the depth measurements incorporated into ENC bathymetric layers.
Machine learning and ensemble approaches are being explored to estimate bathymetry, improving predictive accuracy and robustness. These methods can be trained on existing high-quality bathymetric and hydrographic data to automatically generate or refine features in the ENC workflow.
The CHS is also working on developing new tools to automate the creation of Quality of data features based on the attributes of each cell in the deconflicted combined surface. The workflow consists of Process Models that take simple inputs and execute actions in a precise order using predetermined parameters. Both point clouds are combined to create the final product.
In summary, optimizing the ENC bathymetric feature workflow involves combining precise, high-quality input data with advanced modeling techniques, automated correction processes, and machine learning enhancements within a fully integrated, automated pipeline. Continuous validation against real-world measurements ensures hydrographic products remain reliable and navigationally safe, paving the way for a future where any new product or update to an existing ENC is compiled using automated tools.
- To enhance the ENC production and updating workflows, the CHS intends to delve into the realm of environmental science, focusing on the quality of bathymetric data input.
- Merging multiple data sources, such as local high-resolution surveys and global datasets, will ensure accurate and high-resolution bathymetric data for capturing fine details.
- Advanced numerical modeling and dynamic downscaling will be integrated into the process to refine feature accuracy, improving flood risk assessments and navigation reliability.
- Automated data correction and filtering workflows will be employed to maintain consistent and reliable depth measurements, applying algorithmic corrections for known biases.
- In the pursuit of further optimization, machine learning and ensemble approaches are being investigated to estimate bathymetry, improving predictive accuracy and robustness.
- The CHS endeavors to develop new tools for automating the creation of Quality of data features, basing them on the attributes of each cell in the deconflicted combined surface.
- To foster personal growth, productivity, and learning in the field, the CHS aims to equip its team with the necessary knowledge and skills in data-and-cloud-computing, technology, education-and-self-development, and renewable-energy within the industry, paving the way for viable careers in climate-change mitigation and energy management.
