The hydrographic community stands at a pivotal moment where machine learning/AI applications offer transformative potential. However, this potential can only be realized through coordinated, purposeful collaboration. We need your expertise and commitment to:
- Build confidence in machine learning/AI applications across the hydrographic community
- Establish standardized metrics and benchmark datasets that enable fair algorithm comparison
- Bridge the critical expertise gap between AI specialists and experts in hydrography
- Address applications across multiple data types (multibeam, SDB, lidar, ICESat-2, ASV imagery etc.)
To facilitate meaningful progress, I've established a GitHub repository where we can:
- Track issues to generate ideas for focused discussion
- Consolidate and organize proposals in one central location
- Identify concrete work items with actual consequences for our field
Repository link: CCOMJHC/MLHWG: Machine Learning in Hydrography Working Group
Please feel free to:
- Add issues for discussion topics you believe deserve our collective attention
- Recruit additional colleagues whose expertise could strengthen our initiative
This is volunteer work that requires commitment, but the potential impact on our field is substantial.
Derrick Peyton has offered that we host a discussion and round table at the Canadian Hydrographic Conference in Montreal next year where we can present our agreements and findings to the broader community. This will be an excellent opportunity to showcase the working group's accomplishments and gather additional input from the hydrographic community.
I look forward to your active participation in this endeavor.