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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146 current initiative(s)

AI initiative registry
ID	AI Initiative	Current Status	Description	Sector	Region	AI Bucket	SME
212	Surveillance électronique en mer	Pilot	Il s’agit de capture d’images vidéo sur les navires de pêche pour ensuite les faire analyser par des ressources humaines et un modèle d’inte...	Statistics, Policy and Licensing	Quebec	Perception and Understanding	Rémillard, Vincent
217	Suite du projet de recherche de recherche et développement d’outils de vision automatisée pour l’étude des refuges marins des coraux et des éponges du golfe du Saint-Laurent.	Pilot	Une entente de contribution pour l’année 2025-2026 (Programme de gestion des océans) visant à l’amélioration de l’outil développé par le Cen...	Marine Planning and Conservation	Quebec	Perception and Understanding	Brossard, Jade
163	Recherches sur l’Intelligence artificielle pour le traitement automatisé des données d’acoustiques sous-marine	Pilot	Nous utilisons les technologies d’apprentissage profond (deep learning) et les détecteurs pour la détection et identification en temps réal...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
216	Recherche et développement d’outils de vision automatisée pour l’étude des refuges marins des coraux et des éponges du golfe du Saint-Laurent.	Pilot	Une entente de contribution a pour l’année 2024-2025 a été signée avec le Centre de développement et de recherche en intelligence numérique...	Marine Planning and Conservation	Quebec	Perception and Understanding	Brossard, Jade
215	Projet pilote d’utilisation de M365 Copilot, pour le programme de Protection du poisson et de son habitat (PPPH / FFHPP)	Pilot	Utilisation de Microsoft 365 Copilot dans le cadre de la modernisation du PPPH Réflexions sur les possibilités d’optimiser les processus d...	Chief Digital Officer	Quebec	Cognitive and Generative AI	Roy, Vincent
213	Numérisation de documents manuscrits	Pilot	En collaboration avec l’équipe nationale de l’Intendant principal des données (PSSI), visant principalement la pêche au saumon. L’objectif e...	Statistics, Policy and Licensing	Quebec	Perception and Understanding	Rémillard, Vincent
1	M365 Copilot Pilot	Pilot	We are running the Copilot Licensed pilot to see the use of Copilot as an elevated assistant. We are looking into how productive it can help...	Chief Digital Officer	National Capital Region	Cognitive and Generative AI	Tremblay, Julien
3	Labels Pilot	Pilot	We are looking into using Labels back end for smart labels as well as a free copilot agent for the departement to use in order to validate a...	Chief Digital Officer	National Capital Region	Automation and Decision Support	Bartoli, Saul
218	L’observation des bélugas de l’estuaire du Saint-Laurent	Pilot	Entente de contribution avec le Groupe de recherche et d'éducation sur les mammifères marins (GREMM) pour le développement d'un module de me...	Marine Planning and Conservation	Quebec	Perception and Understanding	Robert, Sonia
92	Identification automatisée de la condition de carapace de crabe des neiges	Pilot	Détermination de la condition de carapace de crabe des neiges à partir d’images. La méthode offre une façon de valider/améliorer des données...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
183	General - Office Management	Pilot	Draft and Refine Documents: Staff make use of Copilot to generate or refine briefing notes to make these clear and concise. Enhance Collabor...	Programs Sector	National Capital Region	Cognitive and Generative AI	Fernandes, Ashley
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
208	Fishing Detection	Pilot	The goal is to use vessel movement behaviour in the form of AIS data to predict whether a vessel is currently engaged in fishing activity. A...	Strategic Policy	National Capital Region	Automation and Decision Support	Croft, Lee
93	Détection de homards et de crabes communs dans les images sous-marines par intelligence artificielle.	Pilot	Développer un processus de capture et d’analyse d’images sous-marine par Intelligence Artificielle pour la détection de homards et de crabes...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
166	Détection de baleines sur les images obtenues par les drones	Pilot	Ce projet vise à générer des bases de données d’images annotées et des modèles IA entrainés pour détecter les différentes espèces ou groupes...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
211	Analyse des sentiments	Pilot	C’est un modèle pour traiter automatiquement des textes et documents audios pour classifier les sentiments qui en ressortent sur le sujet....	Strategic Policy	Quebec	Automation and Decision Support	Brunelle, Charles
180	Pacific Aquaculture Regulations (PAR) Gap Analysis	Pilot	Copilot is being leveraged to analyze written submissions and meeting notes from the Transition Plan engagement process to identify key them...	Programs Sector	National Capital Region	Cognitive and Generative AI	Andrade, Margaret
96	Use of machine-learning (ML) algorithms and high-performance computing (HPC) for the optimization of satellite image analysis for mapping coastal habitats	Pilot	This project aims to improve satellite image analysis of marine habitats leveraging NRC HPC and ML assets to apply cutting-edge ML technique...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
89	Augmenting Whale Detection in Satellite Images using synthetic data	Pilot	An autodetector model was built to detect whales in satellite imagery using synthetic imagery. The primary author is now testing the algorit...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
95	Optimization of Machine Learning modelling strategy for coastal zooplankton taxonomic identification from flow microscopy imaging.	Pilot	As part of Aquaculture Monitoring Program we are using Ecotaxa's ML algorithm to identify zooplankton. The project aims at identifying the b...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
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
87	ANMPA Habitat Mapping Project	Pilot	Drop camera survey to document seafloor habitats and biological communities in the Anguniaqvia niqiqyuam Marine Protected Area (ANMPA) in th...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
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
91	Localization and Tracking of Beluga Whales in Aerial video Using Deep Learning	Pilot	Deep-learning model was developed us YOLOv7 to detect beluga whales from imagery footage. The main contribution of this research is providin...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	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

212
Surveillance électronique en mer
Il s’agit de capture d’images vidéo sur les navires de pêche pour ensuite les faire analyser par des ressources humaines et un modèle d’intelligence artificielle doté de capacité d’identification d’événements, d’espèces, etc. Projet fait en collaboration avec l’équipe nationale de l’Intendant principal des données.

Pilot Statistics, Policy and Licensing Perception and Understanding
217
Suite du projet de recherche de recherche et développement d’outils de vision automatisée pour l’étude des refuges marins des coraux et des éponges du golfe du Saint-Laurent.
Une entente de contribution pour l’année 2025-2026 (Programme de gestion des océans) visant à l’amélioration de l’outil développé par le Centre de développement et de recherche en intelligence numérique (CDRIN) en 2024-2025. Le projet a comme objectif global de concevoir et développer un outil d’intelligence artificielle permettant de détecter et suivre les organismes benthiques filmés dans les refuges marins des coraux et des éponges du Golfe du Saint-Laurent.

Pilot Marine Planning and Conservation Perception and Understanding
163
Recherches sur l’Intelligence artificielle pour le traitement automatisé des données d’acoustiques sous-marine
Nous utilisons les technologies d’apprentissage profond (deep learning) et les détecteurs pour la détection et identification en temps réal des espèces de mammifères marins telles que la baleine noire de l’Atlantique.

Pilot Ecosystems and Oceans Science Perception and Understanding
216
Recherche et développement d’outils de vision automatisée pour l’étude des refuges marins des coraux et des éponges du golfe du Saint-Laurent.
Une entente de contribution a pour l’année 2024-2025 a été signée avec le Centre de développement et de recherche en intelligence numérique (CDRIN) pour concevoir et développer un outil d’intelligence artificielle permettant de détecter et suivre les organismes benthiques filmés dans refuges marins des coraux et des éponges du Golfe du Saint-Laurent. L’entente s’est terminée le 31 mars 2025.

Pilot Marine Planning and Conservation Perception and Understanding
215
Projet pilote d’utilisation de M365 Copilot, pour le programme de Protection du poisson et de son habitat (PPPH / FFHPP)
Utilisation de Microsoft 365 Copilot dans le cadre de la modernisation du PPPH Réflexions sur les possibilités d’optimiser les processus du programme en s’appuyant sur l’utilisation de M365 Copilot.

Pilot Chief Digital Officer Cognitive and Generative AI
213
Numérisation de documents manuscrits
En collaboration avec l’équipe nationale de l’Intendant principal des données (PSSI), visant principalement la pêche au saumon. L’objectif est de numériser des documents avec des données manuscrites et de les faire analyser par un modèle d’intelligence artificielle qui va décoder l’écriture pour la transformer en données numériques et les insérer dans une base de données. Le but est de remplacer la saisie manuelle de ces données.

Pilot Statistics, Policy and Licensing Perception and Understanding
1
M365 Copilot Pilot
We are running the Copilot Licensed pilot to see the use of Copilot as an elevated assistant. We are looking into how productive it can help people perform their work, reduce administratrive tasking We have 300 licenses, but we also have all DFO users available for a smaller version of Copilot that could be leveraged.

Pilot Chief Digital Officer Cognitive and Generative AI
3
Labels Pilot
We are looking into using Labels back end for smart labels as well as a free copilot agent for the departement to use in order to validate a user's classification. if we could fund the "query" fund for the user community we could point the Free agent to consume DFO specific Information on Information Classification. as for the other option we will need to ensure we have items to feed the machine learning.

Pilot Chief Digital Officer Automation and Decision Support
218
L’observation des bélugas de l’estuaire du Saint-Laurent
Entente de contribution avec le Groupe de recherche et d'éducation sur les mammifères marins (GREMM) pour le développement d'un module de mesures morphométriques assistées par l’intelligence artificielle pour les bélugas de l'estuaire du Saint-Laurent L’entente s’est terminée le 31 mars 2024 mais le module est en cours d’utilisation. Considérant la contrainte de temps imposée par les mesures manuelles des images prises par drones de bélugas, l’équipe de recherche du GREMM a développé une méthode exploitant l’IA pour mesurer plus rapidement et plus efficacement les bélugas.

Pilot Marine Planning and Conservation Perception and Understanding
92
Identification automatisée de la condition de carapace de crabe des neiges
Détermination de la condition de carapace de crabe des neiges à partir d’images. La méthode offre une façon de valider/améliorer des données de condition de carapace prises sur le terrain. Ces déterminations sont importantes pour quantifier le recrutement à la pêche durant le relevé scientifique ainsi qu’améliorer la qualité des données de crabe mou dans programme observateur-en-mer.

Pilot Ecosystems and Oceans Science Perception and Understanding
183
General - Office Management
Draft and Refine Documents: Staff make use of Copilot to generate or refine briefing notes to make these clear and concise. Enhance Collaboration and Workflow Efficiency: By offering suggestions for document formatting, language style, and clarifying content, Copilot helps maintain consistency and accuracy across communications and facilitates more effective interdepartmental collaboration. Meeting notes: After a meeting is recorded, Copilot can transcribe audio to produce a relatively accurate record of the discussion, using natural language processing to identify key points, decisions, and follow-up tasks, and organizing these into relatively concise summaries and action item lists. Conduct Research and Summarize Information: It can quickly extract key points from extensive reports, legislation, or guidelines—helping to synthesize complex information into actionable insights. Support Data Analysis and Reporting: Employees use Copilot to structure performance metrics, track progress against key indicators, and generate reports that inform decision-making. **These functionalities save time, improve comprehensiveness, and can help ensure that critical details are captured.**

Pilot Programs Sector Cognitive and Generative AI
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
208
Fishing Detection
The goal is to use vessel movement behaviour in the form of AIS data to predict whether a vessel is currently engaged in fishing activity. AIS data is available to the department in near real-time from AIS transponders equipped on vessels. An automated system that can indicate to fishery officers when and where vessels are likely engaged in fishing activities will support better monitoring of compliance with regulations such as conformance with conditions in fishing licenses and in marine protected areas. Due to limited capacity and lengthy processes to manually review and distill data sources, fishery officers cannot fully monitor all vessels under present circumstances. This AI-supported system would help to increase monitoring coverage for fishery officers. Spatial processing: The vast amounts of geographically-referenced AIS transmissions from vessels must be refined to spatiotemporal areas of interest prior to feature engineering for input to the machine learning model. Appropriate spatial processing can support achieving this in a timely manner, particularly when near real-time processing is of interest. However, given the availability of the AIS pipeline, this processing is more appropriate to be performed by the pipeline than within the analytics environment for this use case. The fishing detection system would use the AIS pipeline API and would received data which has already been refine to the relevant spatial extents.

Pilot Strategic Policy Automation and Decision Support
93
Détection de homards et de crabes communs dans les images sous-marines par intelligence artificielle.
Développer un processus de capture et d’analyse d’images sous-marine par Intelligence Artificielle pour la détection de homards et de crabes communs.

Pilot Ecosystems and Oceans Science Perception and Understanding
166
Détection de baleines sur les images obtenues par les drones
Ce projet vise à générer des bases de données d’images annotées et des modèles IA entrainés pour détecter les différentes espèces ou groupes d’espèces visés.

Pilot Ecosystems and Oceans Science Perception and Understanding
211
Analyse des sentiments
C’est un modèle pour traiter automatiquement des textes et documents audios pour classifier les sentiments qui en ressortent sur le sujet. Un premier test sera fait sur le sujet des nouveaux permis de pêche exploratoire du homard.

Pilot Strategic Policy Automation and Decision Support
180
Pacific Aquaculture Regulations (PAR) Gap Analysis
Copilot is being leveraged to analyze written submissions and meeting notes from the Transition Plan engagement process to identify key themes, concerns, and suggestions. It helps summarise large aounts of input that help detect gaps in the feedback - such as missing perspectives, underrepresented topics, or unclear areas - enabling AD to plan more targeted and inclusive future engagement and consultations on the PAR amendments. Copilot also helps to dig into input in a more manageable way.

Pilot Programs Sector Cognitive and Generative AI
96
Use of machine-learning (ML) algorithms and high-performance computing (HPC) for the optimization of satellite image analysis for mapping coastal habitats
This project aims to improve satellite image analysis of marine habitats leveraging NRC HPC and ML assets to apply cutting-edge ML techniques to the challenges of remote sensing data analysis. Relates to ongoing project (below) through MPC service-level agreement.

Pilot Ecosystems and Oceans Science Perception and Understanding
89
Augmenting Whale Detection in Satellite Images using synthetic data
An autodetector model was built to detect whales in satellite imagery using synthetic imagery. The primary author is now testing the algorithm on satellite imagery obtained by Arctic Region that has been manually read for whales.

Pilot Ecosystems and Oceans Science Perception and Understanding
95
Optimization of Machine Learning modelling strategy for coastal zooplankton taxonomic identification from flow microscopy imaging.
As part of Aquaculture Monitoring Program we are using Ecotaxa's ML algorithm to identify zooplankton. The project aims at identifying the best strategy in building model training sets for each region monitored. This strategy will be the basis for future AMP zooplankton identification work.

Pilot Ecosystems and Oceans Science Perception and Understanding
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
87
ANMPA Habitat Mapping Project
Drop camera survey to document seafloor habitats and biological communities in the Anguniaqvia niqiqyuam Marine Protected Area (ANMPA) in the Inuvialuit Settlement Region. A project sub-objective is to develop AI capacity to detect and record bottom habitat classifications and benthic animals from video still-images, substantially reducing processing times.

Pilot Ecosystems and Oceans Science Perception and Understanding
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
91
Localization and Tracking of Beluga Whales in Aerial video Using Deep Learning
Deep-learning model was developed us YOLOv7 to detect beluga whales from imagery footage. The main contribution of this research is providing a system that accurately detects and tracks features of beluga whales, both adults and calves, from aerial footage.

Pilot Ecosystems and Oceans Science Perception and Understanding
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