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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198 current initiative(s)

AI initiative registry
ID	AI Initiative	Stage	Description	Sector	Region	AI Bucket	SME
169	Dark Vessel Detection Platform	Pilot	Utilizes AI and machine learning to detect vessels in satellite imagery, predict movements, and flag potential IUU fishing globally.Automati...	Programs Sector	National Capital Region	Perception and Understanding	Wheeler, Sean
225	Data Innovation – Pacific Salmon Strategy Initiative	Ideation	Discover innovative data and artificial intelligence (A.I) projects addressing complex salmon data challenges made possible through strategi...	—	Pacific	Infrastructure and Enablement	—
152	Data Validation Tool	Pilot	Use large language models to help detect errors in data transcribed using optical character recognition. Apply validation rules to propose...	Ecosystems and Oceans Science	National Capital Region	Automation and Decision Support	Winegardner, Amanda
70	Data Validation Tool (See B6, B22)	Pilot	Use large language models to help detect errors in data transcribed using optical character recognition. Apply validation rules to propose...	Ecosystems and Oceans Science	Pacific	Automation and Decision Support	Bird, Tom
4	Database Scripting Automation	Ideation	i would like to be able to review the scripts the DBAs run to ensure that we can automate and reduce the administrative workload. if possibl...	Chief Digital Officer	National Capital Region	Automation and Decision Support	Jeanveau, Denis
90	Deep Learning Enhances Beluga Whale Research in the Arctic	Pilot	Esri Canada built a deep learning model that automatically detects beluga whales in imagery. The model was developed using satellite, drone...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
62	Deep sea habitat, organism and object detection and identification.	Ideation	Proposing a capstone project to Computer Science students from Camosun College to develop a pipeline for identifying objects and loading the...	Ecosystems and Oceans Science	Pacific	Perception and Understanding	Best, Merlin
121	Develop a deep-learning model to age capelin otoliths: application for stock assessment	Pilot	Investigate the utility of applying a deep-learning model developed by the DFO Gulf Region for aging fish otoliths; specifically, to determi...	Ecosystems and Oceans Science	National Capital Region	Automation and Decision Support	Winegardner, Amanda
122	Development and testing of artificial intelligence and machine learning to analyze underwater photo and video data to assess marine impacts and resources	Pilot	This completed project (2020/21) aimed to utilize machine learning and imaging technology in fisheries science. The proponent planned to use...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
100	Development of AI technology for otolith and scale ageing	Pilot	Ageing of scales and otoliths is time-consuming, relies on skilled personnel, and often faces delays. AI tools like DFOdots offer a solution...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
164	Development of an abundance index for an under surveyed capelin stock	Ideation	Recherche qui vise à utiliser des méthodes d’apprentissage automatique (machine learning) dans le but de classifier les signaux acoustiques...	Ecosystems and Oceans Science	National Capital Region	Automation and Decision Support	Winegardner, Amanda
88	Development of an AI matching model for bowhead whale photo-identification	Pilot	Using DFO’s existing database of bowhead whale photographs, the specific objectives are: 1) Create a machine learning model “detector” for b...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
220	Development of an underwater videography and AI system for studying fish passage effectiveness	Pilot	Designed to train existing DFO tools with information on fish passage effectiveness and develop an SOP for training videography-based AI mod...	Ecosystems and Oceans Science	Ontario and Prairie	Perception and Understanding	Klein, David
124	Development of an underwater videography and AI system for studying fish passage effectiveness	Pilot	Designed to train existing DFO tools with information on fish passage effectiveness and develop an SOP for training videography-based AI mod...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
168	Development of bespoke data-limited tools using AI coding.	Pilot	The majority of Canadian fish stocks are data limited thus preventing full analytical stock assessments. Data limited tools are therefore th...	Ecosystems and Oceans Science	National Capital Region	Cognitive and Generative AI	Winegardner, Amanda
226	DFO AI - Pipeline	Pilot	DFO Pipeline Analyzer is a private Azure DevOps extension designed to assist development teams in diagnosing and resolving pipeline failures...	Chief Digital Officer	National Capital Region	Infrastructure and Enablement	Mehdi, Ghizal
252	DFO AI Hub	Development	The DFO AI Hub is a centralized digital platform that demonstrates the application of artificial intelligence technologies to fisheries and...	Strategic Policy	Not region specific	Interaction and Engagement	Tran, Tan
94	DFO Bot, deep learning application to analyse otolith images available in DFO Dots	Pilot	Development of a neural network trained using otolith images, expert-derived age estimates, otolith annotations and annuli to obtain age est...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
250	DFO Data Centre Chatbot	Development	• Faster access to Data Centre information and processes • Reduced dependency on SMEs and email-based support • Improved staff onboarding...	Strategic Policy	National Capital Region	Interaction and Engagement	Tran, Tan
229	DFO PB LLM Service	Pilot	Provide an approved DFO internal LLM as a service in a PBMM environment. After our team performed a year-long pilot project providing an unc...	Chief Digital Officer	National Capital Region	Cognitive and Generative AI	Korol, Oksana
111	Digital Twins of the Ocean Task Team	Pilot	National exchanges on topics relating to ocean modelling for development of Digital Twins of the Ocean, including use of AI for modelling an...	Ecosystems and Oceans Science	National Capital Region	Infrastructure and Enablement	Winegardner, Amanda
112	ECCC exchanges on use of AI for ocean modelling	Pilot	Exchanges with ECCC on implementation of machine learning methods for operational ocean forecasting, including potential uses for DFO models...	Ecosystems and Oceans Science	National Capital Region	Infrastructure and Enablement	Winegardner, Amanda
184	Email searches in response to requests	Ideation	Leverage M365 Copilot functionalities to leverage AI to perform email searches in response to certain requests, including those related to A...	Programs Sector	National Capital Region	Automation and Decision Support	Fernandes, Ashley
77	Emulator for OPP port models and for relocatable ocean modelling system	Ideation	Use machine learning to develop an emulator (or similar) for the OPP port models, which would be used to produce forecasts via inference. Th...	Ecosystems and Oceans Science	Pacific	Cognitive and Generative AI	Dunphy, Michael
51	EN/FR Translation Model	Pilot	A large language model that is trained on the Translations Bureau’s and DFO's language-characteristics (i.e. grammar, annotation, syntax, wo...	Programs Sector	Pacific	Interaction and Engagement	Quan, Eric

169
Dark Vessel Detection Platform
Utilizes AI and machine learning to detect vessels in satellite imagery, predict movements, and flag potential IUU fishing globally.Automatically cross-references detected vessels with AIS and VMS data to identify unregistered or "dark" vessels. Supports international IUU fishing monitoring; new AI-driven features enable vessel identification from electro-optical imagery. Governance: Managed by DFO’s International Fisheries Enforcement team; technical oversight by MDA Space; coordinated with CSA and other OGDs. Guardrails: All data use complies with commercial licensing terms and GC privacy requirements; sharing restricted

Pilot Programs Sector Perception and Understanding
225
Data Innovation – Pacific Salmon Strategy Initiative
Discover innovative data and artificial intelligence (A.I) projects addressing complex salmon data challenges made possible through strategic investments from the Pacific Salmon Strategy Initiative (PSSI). PSSI initiatives harness the power of advanced data and A.I. technologies like machine learning, computer vision, and natural language processing to revolutionize how to support salmon conservation and rebuilding efforts in the modern data and digital era. By integrating advanced data and A.I. technologies with program activities such as habitat restoration, ecosystem planning, sustainable fishery management practices, and collaborative efforts in Science, the PSSI leverages modern tools to enhance planning and data-driven decision-making. Together, these data and A.I. efforts create high quality and more accessible data and enable better insights and actions to protect and restore Pacific salmon and its ecosystems.

Ideation Infrastructure and Enablement
152
Data Validation Tool
Use large language models to help detect errors in data transcribed using optical character recognition. Apply validation rules to propose corrections and normalize data into a standardized format.

Pilot Ecosystems and Oceans Science Automation and Decision Support
70
Data Validation Tool (See B6, B22)
Use large language models to help detect errors in data transcribed using optical character recognition. Apply validation rules to propose corrections and normalize data into a standardized format. (Digitizing non-machine-readable (handwritten) documents and extracts characters into a digital format) Llama 3.0 Large Language Model

Pilot Ecosystems and Oceans Science Automation and Decision Support
4
Database Scripting Automation
i would like to be able to review the scripts the DBAs run to ensure that we can automate and reduce the administrative workload. if possible we would then be able to provide more opporutnities for the group to do more architecturing then Administrative updates. haven'T delved this through, but i think it would be high value.

Ideation Chief Digital Officer Automation and Decision Support
90
Deep Learning Enhances Beluga Whale Research in the Arctic
Esri Canada built a deep learning model that automatically detects beluga whales in imagery. The model was developed using satellite, drone and aerial imagery and reported accuracy is high, but the model has not been tested by DFO Science yet.

Pilot Ecosystems and Oceans Science Perception and Understanding
62
Deep sea habitat, organism and object detection and identification.
Proposing a capstone project to Computer Science students from Camosun College to develop a pipeline for identifying objects and loading the results into Biigle for human review. TBD.

Ideation Ecosystems and Oceans Science Perception and Understanding
121
Develop a deep-learning model to age capelin otoliths: application for stock assessment
Investigate the utility of applying a deep-learning model developed by the DFO Gulf Region for aging fish otoliths; specifically, to determine the age of capelin in the 2J3KL stock in Newfoundland for use in stock assessments. Otolith aging is currently done visually using a microscope, this proposed method would automate that process to save time and resources, while reducing human error and bias

Pilot Ecosystems and Oceans Science Automation and Decision Support
122
Development and testing of artificial intelligence and machine learning to analyze underwater photo and video data to assess marine impacts and resources
This completed project (2020/21) aimed to utilize machine learning and imaging technology in fisheries science. The proponent planned to use Video and Image Analytics for Marine Environments (VIAME), an open-source system developed by NOAA, for two specific purposes: 1. Measure the movement of Atlantic cod across boundaries at the Gilbert Bay Marine Protected Area in Newfoundland and Labrador. 2. Assess the impact of seismic surveying on Atlantic cod as part of a project funded by the Environmental Studies Research Fund (ESRF). The automatic detection and count of fish near MPA boundaries was expected to reduce use of personnel time and effort.

Pilot Ecosystems and Oceans Science Perception and Understanding
100
Development of AI technology for otolith and scale ageing
Ageing of scales and otoliths is time-consuming, relies on skilled personnel, and often faces delays. AI tools like DFOdots offer a solution by enabling automated ageing using annotated reference collections, helping reduce backlogs and support training. Developing these AI models can enhance resilience, efficiency, and training capacity in ageing programs.

Pilot Ecosystems and Oceans Science Perception and Understanding
164
Development of an abundance index for an under surveyed capelin stock
Recherche qui vise à utiliser des méthodes d’apprentissage automatique (machine learning) dans le but de classifier les signaux acoustiques du capelan dans le Golfe du St-Laurent.

Ideation Ecosystems and Oceans Science Automation and Decision Support
88
Development of an AI matching model for bowhead whale photo-identification
Using DFO’s existing database of bowhead whale photographs, the specific objectives are: 1) Create a machine learning model “detector” for bowhead whales. 2) Create a machine learning individual re-ID model for bowhead whales to re-identify bowheads. 3) To make the detector and matching models accessible through an easy to use, freely available, web-based application, Flukebook.org.

Pilot Ecosystems and Oceans Science Perception and Understanding
220
Development of an underwater videography and AI system for studying fish passage effectiveness
Designed to train existing DFO tools with information on fish passage effectiveness and develop an SOP for training videography-based AI models in support of fish passage projects.

Pilot Ecosystems and Oceans Science Perception and Understanding
124
Development of an underwater videography and AI system for studying fish passage effectiveness
Designed to train existing DFO tools with information on fish passage effectiveness and develop an SOP for training videography-based AI models in support of fish passage projects.

Pilot Ecosystems and Oceans Science Perception and Understanding
168
Development of bespoke data-limited tools using AI coding.
The majority of Canadian fish stocks are data limited thus preventing full analytical stock assessments. Data limited tools are therefore the only approach. We are engaged in quick Ai development of custom data limited assessment approaches for exploited fish stocks

Pilot Ecosystems and Oceans Science Cognitive and Generative AI
226
DFO AI - Pipeline
DFO Pipeline Analyzer is a private Azure DevOps extension designed to assist development teams in diagnosing and resolving pipeline failures using AI. When a pipeline run fails, this task automatically triggers, extracts the provided error details, and queries a secure AI API to generate a human-readable explanation of the failure along with actionable troubleshooting steps. It will assist developers to make appropriate steps in right direction as they are getting suggestion with-in pipeline logs. Suggestion also provide corrected version of pipeline Key features: Runs only on pipeline failure (condition: failed()) Fully agent-agnostic: Works on self-hosted and Azure-hosted agents, across Windows, Linux, and macOS Provides clear error analysis and resolution guidance directly in pipeline logs Keeps your data secure by using your own OpenAI or other AI API key Fully private and scoped to your Azure DevOps organization We have working prototype of DFO-AI pipeline Analyzer by communicating with OpenAI securely. In next steps we are also exploring to integrate with our self-hosted AI Models (open-source via Ollama), currently in discussion

Pilot Chief Digital Officer Infrastructure and Enablement
252
DFO AI Hub
The DFO AI Hub is a centralized digital platform that demonstrates the application of artificial intelligence technologies to fisheries and oceans research, management, and operations. The platform serves as an educational tool that demonstrates how AI can help addresses business needs for faster, more accurate data analysis, improved decision support, and increased operational efficiency across research, monitoring, and management functions. It enables exploratory use of machine learning, computer vision, and natural language processing to process diverse data sources (e.g., imagery, sensor data, scientific reports), automate routine analytical tasks, and generate actionable insights for policy, compliance, and conservation programs.

Development Strategic Policy Interaction and Engagement
94
DFO Bot, deep learning application to analyse otolith images available in DFO Dots
Development of a neural network trained using otolith images, expert-derived age estimates, otolith annotations and annuli to obtain age estimates from otoliths.

Pilot Ecosystems and Oceans Science Perception and Understanding
250
DFO Data Centre Chatbot
• Faster access to Data Centre information and processes • Reduced dependency on SMEs and email-based support • Improved staff onboarding and operational efficiency • Supports GoC digital-first and AI-enablement direction

Development Strategic Policy Interaction and Engagement
229
DFO PB LLM Service
Provide an approved DFO internal LLM as a service in a PBMM environment. After our team performed a year-long pilot project providing an unclassified internal GenAI models for 14 teams to develop their custom chatbots, we have collected feedback and identified gaps in the availability of GenAI at DFO. One of the biggest gaps was the need for a secure model to be used with private or classified data. Our cloud platform is certified up to PBMM, so we are working to identify, deploy and approve an internal large language model (LLM), as well as make it available to DFO users through API. IT security team is involved in helping us secure and certify the model for use at DFO.

Pilot Chief Digital Officer Cognitive and Generative AI
111
Digital Twins of the Ocean Task Team
National exchanges on topics relating to ocean modelling for development of Digital Twins of the Ocean, including use of AI for modelling and products.

Pilot Ecosystems and Oceans Science Infrastructure and Enablement
112
ECCC exchanges on use of AI for ocean modelling
Exchanges with ECCC on implementation of machine learning methods for operational ocean forecasting, including potential uses for DFO models.

Pilot Ecosystems and Oceans Science Infrastructure and Enablement
184
Email searches in response to requests
Leverage M365 Copilot functionalities to leverage AI to perform email searches in response to certain requests, including those related to ATI/P

Ideation Programs Sector Automation and Decision Support
77
Emulator for OPP port models and for relocatable ocean modelling system
Use machine learning to develop an emulator (or similar) for the OPP port models, which would be used to produce forecasts via inference. The inference is expected to be both faster and computationally cheaper than existing solutions. The training process would use existing model output from multi-year hindcasts. Success with a fixed location would graduate to developing a relocatable emulator TBD; could be U-Net or CNN, or based on GraphCast

Ideation Ecosystems and Oceans Science Cognitive and Generative AI
51
EN/FR Translation Model
A large language model that is trained on the Translations Bureau’s and DFO's language-characteristics (i.e. grammar, annotation, syntax, words, scientific term) context. The model’s enables translation to be like those by the Translation Bureau. Training on large database of scientific documents (published by Canadian Science Advisory Secretariat) to understand scientific terminology is currently in progress. MADLAD 400 10B (Initially trained on sample of ~100 translated documents, in progress of additional training with published CSAS documents going back to year 2000)

Pilot Programs Sector Interaction and Engagement