AI initiative registry

Every initiative listed in the registry can be consulted here, without an account. Each record shows its progress, its milestones and the history of its decisions. Requests still under triage do not appear: they join the registry once reviewed.

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4 current initiative(s)

AI initiative registry
ID	AI Initiative	Stage	Description	Sector	Region	AI Bucket	SME
222	Automated assessment of Crowd Source Bathymetry	Pilot	The Community Hydrography team is working with the University of New Brunswick (UNB) on a tool-kit that will automate the assessment of larg...	Ecosystems and Oceans Science	Ontario and Prairie	Automation and Decision Support	Klein, David
127	Automated assessment of Crowd Source Bathymetry	Pilot	The Community Hydrography team is working with the University of New Brunswick (UNB) on a tool-kit that will automate the assessment of larg...	Ecosystems and Oceans Science	National Capital Region	Automation and Decision Support	Winegardner, Amanda
61	CTD data QC using ML	Pilot	Working with CDOS office to streamline our CTD QC using ML cnn and others	Ecosystems and Oceans Science	Pacific	Automation and Decision Support	Guan, Lu
31	Quality control of oceanographic data - Maritimes Region	Pilot	The goal of the project is to assist in the processing and quality control of CTD (oceanographic) data by leveraging machine learning models...	Strategic Policy	National Capital Region	Automation and Decision Support	Croft, Lee

222
Automated assessment of Crowd Source Bathymetry
The Community Hydrography team is working with the University of New Brunswick (UNB) on a tool-kit that will automate the assessment of large volumes of crowd source data. AI routines will allow for automated QC, and drafting of Navigation Warnings.

Pilot Ecosystems and Oceans Science Automation and Decision Support
127
Automated assessment of Crowd Source Bathymetry
The Community Hydrography team is working with the University of New Brunswick (UNB) on a tool-kit that will automate the assessment of large volumes of crowd source data. AI routines will allow for automated QC, and drafting of Navigation Warnings.

Pilot Ecosystems and Oceans Science Automation and Decision Support
61
CTD data QC using ML
Working with CDOS office to streamline our CTD QC using ML cnn and others

Pilot Ecosystems and Oceans Science Automation and Decision Support
31
Quality control of oceanographic data - Maritimes Region
The goal of the project is to assist in the processing and quality control of CTD (oceanographic) data by leveraging machine learning models to predict quality flags for the CTD data products generated by Maritimes region, enabling faster data processing by reducing manual burden. Initial explorations have been conducted to investigate transferability of existing quality control models from Pacific region. Based on investigation results, new models will be trained specific to Maritimes region. Client: Maritimes Region - Ocean Data

Pilot Strategic Policy Automation and Decision Support