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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63 current initiative(s)

AI initiative registry
ID	AI Initiative	Stage	Description	Sector	Region	AI Bucket	SME
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 Computer Vision	Bird, Tom
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 Computer Vision	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 Computer Vision	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 Computer Vision	Dupuis, Marc
212	Surveillance électronique en mer Not translated · original: French	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 Computer Vision	Rémillard, Vincent
213	Numérisation de documents manuscrits Not translated · original: French	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 Computer Vision	Rémillard, Vincent
218	L’observation des bélugas de l’estuaire du Saint-Laurent Not translated · original: French	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 Computer Vision	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. Not translated · original: French	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 Computer Vision	Brossard, Jade
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 Computer Vision	Winegardner, Amanda
92	Identification automatisée de la condition de carapace de crabe des neiges Not translated · original: French	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 Computer Vision	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 Computer Vision	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 Computer Vision	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 Computer Vision	Winegardner, Amanda
86	Automatic detection of Arctic whales in aerial images	Pilot	In collaboration with Dr. David Clausi and his team at the Vision and Image Processing (VIP) Research Lab (University of Waterloo), we have...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding Computer Vision	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 Computer Vision	Winegardner, Amanda
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 Computer Vision	Winegardner, Amanda
97	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	National Capital Region	Perception and Understanding Computer Vision	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 Computer Vision	Winegardner, Amanda
103	Improved understanding of the processes impacting harmful algal blooms	Pilot	Using machine learning algorithms to analyze phytoplankton images from the Imaging Flow Cytobot (IFCB) to recognize and quantify species ass...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding Computer Vision	Winegardner, Amanda
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 Computer Vision	Klein, David
119	Computer vision to automate fish data extraction from video	Pilot	Our purpose has been to build a tool to broadly apply computer vision methods in marine research. Our work has generated several publication...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding Computer Vision	Winegardner, Amanda
104	Zooplankton classification from Video Plankton Recorder	Pilot	Using machine learning algorithms to identify plankton for rapid zooplankton classification from the Video Plankton Recorder, in support of...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding Computer Vision	Winegardner, Amanda
118	Use of artificial intelligence models to facilitate seafloor video annotations in conservation areas.	Pilot	The purpose of this initiative is to utilize AI to facilitate seafloor video annotations collected as part of the MCT and Benthic Ecology pr...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding Computer Vision	Winegardner, Amanda
105	Combining artificial intelligence and drone imagery for automatic extraction of morphometric attributes of small cetaceans: Application to the St. Lawrence Estuary Beluga population.	Pilot	This project proposes to use a multiyear time series of drone images of the St. Lawrence Estuary Beluga population to develop a series of al...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding Computer Vision	Winegardner, Amanda
106	Finwave: an online photo-identification database and AI matching tool	Pilot	This project aims to improve a publicly accessible machine learning platform capable of identifying individual killer whales in real-time...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding Computer Vision	Winegardner, Amanda

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 Computer Vision
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 Computer Vision
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 Computer Vision
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 Computer Vision
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 Computer Vision
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 Computer Vision
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 Computer Vision
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 Computer Vision
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 Computer Vision
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 Computer Vision
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 Computer Vision
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 Computer Vision
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 Computer Vision
86
Automatic detection of Arctic whales in aerial images
In collaboration with Dr. David Clausi and his team at the Vision and Image Processing (VIP) Research Lab (University of Waterloo), we have been developing an algorithm to detect narwhals and belugas in photos from aerial surveys. We are planning to expand to ice seals and walrus.

Pilot Ecosystems and Oceans Science Perception and Understanding Computer Vision
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 Computer Vision
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 Computer Vision
97
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 Computer Vision
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 Computer Vision
103
Improved understanding of the processes impacting harmful algal blooms
Using machine learning algorithms to analyze phytoplankton images from the Imaging Flow Cytobot (IFCB) to recognize and quantify species associated with harmful algal blooms.

Pilot Ecosystems and Oceans Science Perception and Understanding Computer Vision
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 Computer Vision
119
Computer vision to automate fish data extraction from video
Our purpose has been to build a tool to broadly apply computer vision methods in marine research. Our work has generated several publications illustrating how we built, tested/validated, and applied computer vision methodology to collect / extract / and analyze data pertaining to marine fish in the Newfoundland and Labrador region, from video. For example, we have used it to examine the impact of seismic surveying on commercial fish in the Newfoundland and Labrador offshore. Our continuing purpose is now broadly sharing our system with commercial harvesters (fishermen), non-profit organization (AHOI), government researchers (aquaculture, MPAs, AIS), and academics (computing scientists), to collect and rapidly analyzing video data for a wide range of applications.

Pilot Ecosystems and Oceans Science Perception and Understanding Computer Vision
104
Zooplankton classification from Video Plankton Recorder
Using machine learning algorithms to identify plankton for rapid zooplankton classification from the Video Plankton Recorder, in support of efforts to assess North Atlantic Right Whale foraging habitat.

Pilot Ecosystems and Oceans Science Perception and Understanding Computer Vision
118
Use of artificial intelligence models to facilitate seafloor video annotations in conservation areas.
The purpose of this initiative is to utilize AI to facilitate seafloor video annotations collected as part of the MCT and Benthic Ecology programs in the NL Region. A large number of vidEcosystems and Oceans Science is collected annually, and manual video annotation (e.g., locating and counting observations) is extremely time-consuming. A published “object detection model” (FathomNet Megalodon Detector, YOLOv8x) trained using marine taxa by MBARI is currently being used to identify objects in our seafloor images. The model does not identify the objects (e.g., to species), but it largely accelerates the process given that locating objects is the most time-consuming part of the work.

Pilot Ecosystems and Oceans Science Perception and Understanding Computer Vision
105
Combining artificial intelligence and drone imagery for automatic extraction of morphometric attributes of small cetaceans: Application to the St. Lawrence Estuary Beluga population.
This project proposes to use a multiyear time series of drone images of the St. Lawrence Estuary Beluga population to develop a series of algorithms for automatic extraction of morphometric information of individual beluga whales using AI.

Pilot Ecosystems and Oceans Science Perception and Understanding Computer Vision
106
Finwave: an online photo-identification database and AI matching tool
This project aims to improve a publicly accessible machine learning platform capable of identifying individual killer whales in real-time from submitted photographs. The ultimate goal is to improve the web-based interface and validate the algorithm used for photo identification against known individuals.

Pilot Ecosystems and Oceans Science Perception and Understanding Computer Vision