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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82 initiative(s)

AI initiative registry
ID	AI Initiative	Current Status	Description	Sector	Region	AI Bucket	SME
245	Object, Classification, Tagging, Organization, and Pattern Understanding Solution (OCTOPUS)	Proof of Concept	This initiative leverages AI technologies to build an Object, Classification, Tagging, Organization, and Pattern Understanding Solution (OCT...	Chief Digital Officer	National Capital Region	Perception and Understanding	Ibrahim, Omar
240	The Current	Proof of Concept	Don't let your harvested data get stuck in still water. Navigate new data streams with AI automation, augmentation and information enablemen...	Fisheries Management	Not region specific	Perception and Understanding	Bird, Tom
228	SILScanner	Pilot	Deploy on DFO's PBMM Cloud a proof of concept application that uses optical character recognition (OCR) to digitize and validate Stream Insp...	Chief Digital Officer	National Capital Region	Perception and Understanding	Bird, Tom
224	Remote Observation of Watercraft on Roads	Pilot	The Aquatic Invasive Species Program is proposing to launch a pilot initiative aimed at addressing a critical knowledge gap in our understan...	—	Ontario and Prairie	Perception and Understanding	Klein, David
223	Automated assessment of satellite imagery	Pilot	AI routines are being developed and tested to assess large volume of satellite imagery and data to: i) Automatically Identify marine hazards...	Ecosystems and Oceans Science	Ontario and Prairie	Perception and Understanding	Klein, David
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
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
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
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
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
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
198	Satellite image analysis for mapping eelgrass habitat in the southern Gulf of St. Lawrence.	Pilot	This project supports Gulf Region needs for spatial data representing eelgrass habitat through a service level agreement with Marine Plannin...	Ecosystems and Oceans Science	Gulf	Perception and Understanding	Dupuis, Marc
197	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	Gulf	Perception and Understanding	Dupuis, Marc
196	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	Gulf	Perception and Understanding	Dupuis, Marc
195	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	Gulf	Perception and Understanding	Dupuis, Marc
192	Harbour Assessments	On hold On hold	Automatically and continuously assess harbour conditions via drone and satellite imagery Solution being developed for Small Craft harbours t...	Indigenous Affairs, Aquaculture and Governance	Gulf	Perception and Understanding	Dupuis, Marc
191	Irish Moss Research	On hold On hold	Automatically and non-invasively detect and assess health of Irish Moss Solution being developed for Science to review hundreds of hours of...	Indigenous Affairs, Aquaculture and Governance	Gulf	Perception and Understanding	Dupuis, Marc
190	Lobster Stock Assessment	Pilot	Automatically assess lobster count and habitat from hours of video efficiently Solution being developed for Science to review hundreds of ho...	Indigenous Affairs, Aquaculture and Governance	Gulf	Perception and Understanding	Dupuis, Marc
189	Site Assessments	On hold On hold	Automatically assess presence of eel grass from drone video Solution being developped to assist Fish and Fish Habitat Protection Program in...	Indigenous Affairs, Aquaculture and Governance	Gulf	Perception and Understanding	Dupuis, Marc
188	Coastal Surveillance	Pilot	Automatically process thousands of drone images as part of continuous surveillance Solution being developed in region to assist C&P in batch...	Indigenous Affairs, Aquaculture and Governance	Gulf	Perception and Understanding	Dupuis, Marc
187	Atlantic Bluefin Tuna Monitoring	Pilot	Automatically perform video analytics from hundred of harvester tuna video Solution being developed in region to assist C&P in batch process...	Indigenous Affairs, Aquaculture and Governance	Gulf	Perception and Understanding	Dupuis, Marc
174	AI model for Video review	Idea	An AI model that reviews video monitoring footage for a fishery or given vessel and machine learns potential events of non compliance	Programs Sector	National Capital Region	Perception and Understanding	Bédard, Mathieu
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
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
165	AIMMS (Artificial Intelligence for Marine Mammals in Survey imagery)	Proof of Concept	The AIMMS project develops a platform to automate and accelerate the interpretation of aerial imagery for marine mammal population assessmen...	Fisheries Management	National Capital Region	Perception and Understanding	Mosnier, Arnaud

245
Object, Classification, Tagging, Organization, and Pattern Understanding Solution (OCTOPUS)
This initiative leverages AI technologies to build an Object, Classification, Tagging, Organization, and Pattern Understanding Solution (OCTOPUS) to enhance the analysis of video, image, and audio data, improve monitoring capabilities and support decision-making processes at DFO. The MVP will initially focus on video processing capabilities, with planned expansion to image and audio analysis in FY 2026–27. In addition, the initiative will deliver integrated data annotation and model training tools to support continuous improvement and reuse of AI models.

Proof of Concept Chief Digital Officer Perception and Understanding
240
The Current
Don't let your harvested data get stuck in still water. Navigate new data streams with AI automation, augmentation and information enablement. The Current is a cloud-hosted platform that enables users anywhere in the department to ingest large volumes of structured and unstructured documents, extract data using optical character recognition and large language models, validate and review results through human-in-the-loop workflows, and relate extracted information using shared concepts and standards. The architecture emphasizes asynchronous processing, modular extraction services, strong traceability, and governance-ready design. The platform supports configurable extraction tasks, versioned models and definitions, auditable review processes, and future discovery of related research outputs and datasets across heterogeneous sources. As the initial use-case, the Text Intelligence and Data Extraction (TIDE) Service has been developed for the Salmon Habitat Restoration (SHARE) System. TIDE is a baseline iteration of The Current that fulfills the minimum viable product requirements of SHARE. TIDE focuses on digitizing, extracting, and modeling data from large unstructured text documents. TIDE establishes the baseline architecture and demonstrates a scalable workflow in Production.

Proof of Concept Fisheries Management Perception and Understanding
228
SILScanner
Deploy on DFO's PBMM Cloud a proof of concept application that uses optical character recognition (OCR) to digitize and validate Stream Inspection Logs (SILs). The PoC was originally developed on SSC's cloud infrastructure. It is now being deployed in DFO PB cloud infrastructure. BLADES initiative and IT security teams are involved.

Pilot Chief Digital Officer Perception and Understanding
224
Remote Observation of Watercraft on Roads
The Aquatic Invasive Species Program is proposing to launch a pilot initiative aimed at addressing a critical knowledge gap in our understanding the spread of aquatic invasive species. Invasive species can attach to boats, trailers, and related equipment—a process commonly referred to as the "stowaway pathway"—which enables their unintentional spread across ecosystems when watercraft are transported between water bodies. The AIS Program is seeking to understand watercraft movement into Canada via international land border crossings. Currently, the extent of this "stowaway" pathway remains largely unquantified. To address this, the pilot will deploy remote camera systems at select border entry points to capture imagery of incoming vehicles. Leveraging machine learning, the system will automatically identify and flag images containing watercraft. This data-driven approach will provide actionable insights to inform strategic decisions on the timing and placement of watercraft inspection and decontamination resources. This initiative may represent a significant step toward enhancing our ability to proactively manage aquatic invasive species risks at key entry points.

Pilot Perception and Understanding
223
Automated assessment of satellite imagery
AI routines are being developed and tested to assess large volume of satellite imagery and data to: i) Automatically Identify marine hazards and ii) Change detection – i.e. shorelines or riverbank changes.

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
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
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
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
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
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
198
Satellite image analysis for mapping eelgrass habitat in the southern Gulf of St. Lawrence.
This project supports Gulf Region needs for spatial data representing eelgrass habitat through a service level agreement with Marine Planning and Conservation. Machine learning algorithms have been applied for classification of satellite imagery. MPC and Science have been collaborating for several years, annually purchasing satellite imagery, with classification conducted by both groups.

Pilot Ecosystems and Oceans Science Perception and Understanding
197
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
196
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
195
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
192
Harbour Assessments
Automatically and continuously assess harbour conditions via drone and satellite imagery Solution being developed for Small Craft harbours to assess harbour conditions (presence of ice, usage, vessel and vehicle traffic, change detection) based on drone footage and satellite imagery.

On hold On hold Indigenous Affairs, Aquaculture and Governance Perception and Understanding
191
Irish Moss Research
Automatically and non-invasively detect and assess health of Irish Moss Solution being developed for Science to review hundreds of hours of video for Irish Moss presence and health.

On hold On hold Indigenous Affairs, Aquaculture and Governance Perception and Understanding
190
Lobster Stock Assessment
Automatically assess lobster count and habitat from hours of video efficiently Solution being developed for Science to review hundreds of hours of video for lobster count and state of habitat.

Pilot Indigenous Affairs, Aquaculture and Governance Perception and Understanding
189
Site Assessments
Automatically assess presence of eel grass from drone video Solution being developped to assist Fish and Fish Habitat Protection Program in reviewing hundreds of hours of drone footage to detect presence of Eel Grass.

On hold On hold Indigenous Affairs, Aquaculture and Governance Perception and Understanding
188
Coastal Surveillance
Automatically process thousands of drone images as part of continuous surveillance Solution being developed in region to assist C&P in batch processing of drone surveillance images.

Pilot Indigenous Affairs, Aquaculture and Governance Perception and Understanding
187
Atlantic Bluefin Tuna Monitoring
Automatically perform video analytics from hundred of harvester tuna video Solution being developed in region to assist C&P in batch processing of at-sea video monitoring footage.

Pilot Indigenous Affairs, Aquaculture and Governance Perception and Understanding
174
AI model for Video review
An AI model that reviews video monitoring footage for a fishery or given vessel and machine learns potential events of non compliance

Idea Programs Sector Perception and Understanding
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
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
165
AIMMS (Artificial Intelligence for Marine Mammals in Survey imagery)
The AIMMS project develops a platform to automate and accelerate the interpretation of aerial imagery for marine mammal population assessments. By streamlining annotation and review, and enabling scientists to develop custom machine-learning models without advanced coding skills, the AIMMS workflow significantly reduce processing time, accelerate delivery of science advice, and lower operational costs.

Proof of Concept Fisheries Management Perception and Understanding