Registre des initiatives en IA

Toutes les initiatives inscrites au registre sont consultables ici, sans compte. Chaque fiche montre son avancement, ses jalons et l'historique de ses décisions. Les demandes encore en cours de triage n'y figurent pas : elles rejoignent le registre une fois examinées.

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146 initiative(s) courante(s)

Registre des initiatives en IA
ID	Initiative en IA	Statut actuel	Description	Secteur	Région	Catégorie d'IA	Expert en la matière
121	Develop a deep-learning model to age capelin otoliths: application for stock assessment Non traduit	Pilot	Investigate the utility of applying a deep-learning model developed by the DFO Gulf Region for aging fish otoliths; specifically, to determi...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Automatisation et aide à la décision	Winegardner, Amanda
120	Characterizing areas of convergence in the Saint John Harbour Non traduit	Pilot	This project is using a technique called self-organizing maps, which is an unsupervised machine learning algorithm, to identify patterns in...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Automatisation et aide à la décision	Winegardner, Amanda
119	Computer vision to automate fish data extraction from video Non traduit	Pilot	Our purpose has been to build a tool to broadly apply computer vision methods in marine research. Our work has generated several publication...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
118	Use of artificial intelligence models to facilitate seafloor video annotations in conservation areas. Non traduit	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...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
116	BinderBot: A Smart Planning Binder Non traduit	Pilot	Employs AI-driven extraction to identify and compile the most relevant data from technical documents for the planning binder. Streamlines t...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Automatisation et aide à la décision	Winegardner, Amanda
115	PublicLens: Science in Plain Language Non traduit	Pilot	Leverages AI to transform highly technical scientific and documents into accessible, plain-language summaries for the Canadian public. Foste...	Sciences des écosystèmes et des océans	Région de la capitale nationale	IA cognitive et générative	Winegardner, Amanda
114	InsightBridge: Executive CSAS Summaries Non traduit	Pilot	Uses AI to convert complex, technical CSAS reports into concise, plain-language summaries tailored for executive decision-makers. Enhances...	Sciences des écosystèmes et des océans	Région de la capitale nationale	IA cognitive et générative	Winegardner, Amanda
113	Improving forecasting for high-priority ports Non traduit	Pilot	Research into feasibility of improving accuracy and timeliness of short-term forecasts for coastal regions by combining traditional and AI m...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Automatisation et aide à la décision	Winegardner, Amanda
112	ECCC exchanges on use of AI for ocean modelling Non traduit	Pilot	Exchanges with ECCC on implementation of machine learning methods for operational ocean forecasting, including potential uses for DFO models...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Infrastructure et habilitation	Winegardner, Amanda
111	Digital Twins of the Ocean Task Team Non traduit	Pilot	National exchanges on topics relating to ocean modelling for development of Digital Twins of the Ocean, including use of AI for modelling an...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Infrastructure et habilitation	Winegardner, Amanda
110	Mercator Collaboration on Digital Twins of the Ocean Non traduit	Pilot	Knowledge exchanges with ECCC and Mercator Océan International on use of AI for ocean modelling, in the context of Digital Twins of the Ocea...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Infrastructure et habilitation	Winegardner, Amanda
109	Advancing Support Systems to Understand the Ocean using AI Non traduit	Pilot	A workshop will be hosted to identify international best practices and key considerations for using AI technologies to understand and predic...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Infrastructure et habilitation	Winegardner, Amanda
108	The development of rapid multiplex qPCR for the detection and quantification of three parasites MSX, SSO, Dermo of oysters (Crassostrea virginica) and MSX single-cell whole genome sequencing using long read sequencing platform’ Non traduit	Pilot	This 2-year project aims to enhance the disease diagnostic services provided to the shellfish aquaculture industry by improving the methods...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Automatisation et aide à la décision	Winegardner, Amanda
107	AI tools for translations Non traduit	Pilot	AI translation tools for all non project-specific communications (e.g., emails, meeting notes, internal taskings). Additional AI tools are n...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Interaction et engagement	Winegardner, Amanda
106	Finwave: an online photo-identification database and AI matching tool Non traduit	Pilot	This project aims to improve a publicly accessible machine learning platform capable of identifying individual killer whales in real-time...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	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. Non traduit	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...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
104	Zooplankton classification from Video Plankton Recorder Non traduit	Pilot	Using machine learning algorithms to identify plankton for rapid zooplankton classification from the Video Plankton Recorder, in support of...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
103	Improved understanding of the processes impacting harmful algal blooms Non traduit	Pilot	Using machine learning algorithms to analyze phytoplankton images from the Imaging Flow Cytobot (IFCB) to recognize and quantify species ass...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
102	Past, Present and Future Oceanographic Conditions in Canada's MPAs and Marine OECMs (Other Effective area-based Conservation Measures) Non traduit	Pilot	Downscaling climate models using neural networks for conservation areas. Neural networks allow us to represent coarse resolution climate pro...	Sciences des écosystèmes et des océans	Région de la capitale nationale	IA cognitive et générative	Winegardner, Amanda
101	Creating space-time continuous surface chlorophyll fields for northwest Atlantic using neural network methods Non traduit	Pilot	Neural network methods are applied to fill the space-time gaps in satellite remote sensing data of surface chlorophyll for the Northwest Atl...	Sciences des écosystèmes et des océans	Région de la capitale nationale	IA cognitive et générative	Winegardner, Amanda
100	Development of AI technology for otolith and scale ageing Non traduit	Pilot	Ageing of scales and otoliths is time-consuming, relies on skilled personnel, and often faces delays. AI tools like DFOdots offer a solution...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
99	Improved freshwater runoff modelling in Eastern Canada to drive ocean models Non traduit	Pilot	Use Neural Networks to post-process streamflow simulations from the WRF-Hydro model, improving their agreement with observational data. Thes...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Automatisation et aide à la décision	Winegardner, Amanda
98	Guidance on Optimal Timing for Environmental DNA (GOTeDNA) Non traduit	Pilot	Provide guidance on optimal eDNA sampling periods and standardized sampling procedures for assessing and monitoring coastal species using eD...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Automatisation et aide à la décision	Winegardner, Amanda
97	Satellite image analysis for mapping eelgrass habitat in the southern Gulf of St. Lawrence. Non traduit	Pilot	This project supports Gulf Region needs for spatial data representing eelgrass habitat through a service level agreement with Marine Plannin...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	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 Non traduit	Pilot	This project aims to improve satellite image analysis of marine habitats leveraging NRC HPC and ML assets to apply cutting-edge ML technique...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda

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 Sciences des écosystèmes et des océans Automatisation et aide à la décision
120
Characterizing areas of convergence in the Saint John Harbour
This project is using a technique called self-organizing maps, which is an unsupervised machine learning algorithm, to identify patterns in areas of convergence near the Saint John Harbour. The preliminary analysis suggests that these patterns are related to different environmental conditions such as river discharge and tidal phase. The work has been conducted in collaboration with scientists from the Maritimes Region and is currently on hold.

Pilot Sciences des écosystèmes et des océans Automatisation et aide à la décision
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension
116
BinderBot: A Smart Planning Binder
Employs AI-driven extraction to identify and compile the most relevant data from technical documents for the planning binder. Streamlines the planning binder process by automatically assembling targeted, information ready for executive review. Saves valuable administrative time and ensures that binder materials are both comprehensive and user-friendly.

Pilot Sciences des écosystèmes et des océans Automatisation et aide à la décision
115
PublicLens: Science in Plain Language
Leverages AI to transform highly technical scientific and documents into accessible, plain-language summaries for the Canadian public. Fosters transparency and builds public trust by making scientific and government information easily understandable. Promotes civic engagement by breaking down barriers between experts and everyday Canadians.

Pilot Sciences des écosystèmes et des océans IA cognitive et générative
114
InsightBridge: Executive CSAS Summaries
Uses AI to convert complex, technical CSAS reports into concise, plain-language summaries tailored for executive decision-makers. Enhances leadership understanding and accelerates informed decision-making by providing easily digestible insights. Ensures executives can quickly grasp critical information without sifting through dense documentation.

Pilot Sciences des écosystèmes et des océans IA cognitive et générative
113
Improving forecasting for high-priority ports
Research into feasibility of improving accuracy and timeliness of short-term forecasts for coastal regions by combining traditional and AI modelling methods.

Pilot Sciences des écosystèmes et des océans Automatisation et aide à la décision
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 Sciences des écosystèmes et des océans Infrastructure et habilitation
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 Sciences des écosystèmes et des océans Infrastructure et habilitation
110
Mercator Collaboration on Digital Twins of the Ocean
Knowledge exchanges with ECCC and Mercator Océan International on use of AI for ocean modelling, in the context of Digital Twins of the Ocean.

Pilot Sciences des écosystèmes et des océans Infrastructure et habilitation
109
Advancing Support Systems to Understand the Ocean using AI
A workshop will be hosted to identify international best practices and key considerations for using AI technologies to understand and predict the ocean.

Pilot Sciences des écosystèmes et des océans Infrastructure et habilitation
108
The development of rapid multiplex qPCR for the detection and quantification of three parasites MSX, SSO, Dermo of oysters (Crassostrea virginica) and MSX single-cell whole genome sequencing using long read sequencing platform’
This 2-year project aims to enhance the disease diagnostic services provided to the shellfish aquaculture industry by improving the methods for detecting economically important Oyster parasites, enhance understanding of Multinucleate Sphere Unknown X (MSX) virulence dynamics, and support the creation of new vaccine in investigating future outbreaks and modeling through machine learning.

Pilot Sciences des écosystèmes et des océans Automatisation et aide à la décision
107
AI tools for translations
AI translation tools for all non project-specific communications (e.g., emails, meeting notes, internal taskings). Additional AI tools are needed to translate larger projects, as relying on the Translation Bureau is costly and introduces delays in deliverables.

Pilot Sciences des écosystèmes et des océans Interaction et engagement
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension
102
Past, Present and Future Oceanographic Conditions in Canada's MPAs and Marine OECMs (Other Effective area-based Conservation Measures)
Downscaling climate models using neural networks for conservation areas. Neural networks allow us to represent coarse resolution climate projections at a much higher spatial resolution across six eastern MPAs.

Pilot Sciences des écosystèmes et des océans IA cognitive et générative
101
Creating space-time continuous surface chlorophyll fields for northwest Atlantic using neural network methods
Neural network methods are applied to fill the space-time gaps in satellite remote sensing data of surface chlorophyll for the Northwest Atlantic. The generated dataset will be validated and analyzed.

Pilot Sciences des écosystèmes et des océans IA cognitive et générative
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 Sciences des écosystèmes et des océans Perception et compréhension
99
Improved freshwater runoff modelling in Eastern Canada to drive ocean models
Use Neural Networks to post-process streamflow simulations from the WRF-Hydro model, improving their agreement with observational data. These neural networks are applied as a downstream calibration step. Builds synergy between traditional calibration methods and modern AI techniques in hydrologic modeling.

Pilot Sciences des écosystèmes et des océans Automatisation et aide à la décision
98
Guidance on Optimal Timing for Environmental DNA (GOTeDNA)
Provide guidance on optimal eDNA sampling periods and standardized sampling procedures for assessing and monitoring coastal species using eDNA. GOTeDNA, a centralized interactive online tool currently in development, will report/visualize trends in spatio-temporal eDNA distributions.

Pilot Sciences des écosystèmes et des océans Automatisation et aide à la décision
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension