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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82 initiative(s)

Registre des initiatives en IA
ID	Initiative en IA	Statut actuel	Description	Secteur	Région	Catégorie d'IA	Expert en la matière
165	AIMMS (Artificial Intelligence for Marine Mammals in Survey imagery) Non traduit	Proof of Concept	The AIMMS project develops a platform to automate and accelerate the interpretation of aerial imagery for marine mammal population assessmen...	Gestion des pêches	Région de la capitale nationale	Perception et compréhension	Mosnier, Arnaud
240	The Current Non traduit	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...	Gestion des pêches	Sans région précise	Perception et compréhension	Bird, Tom
245	Object, Classification, Tagging, Organization, and Pattern Understanding Solution (OCTOPUS) Non traduit	Proof of Concept	This initiative leverages AI technologies to build an Object, Classification, Tagging, Organization, and Pattern Understanding Solution (OCT...	Dirigeant principal du numérique	Région de la capitale nationale	Perception et compréhension	Ibrahim, Omar
23	Video Summarization for Aerial Surveillance Non traduit	Pilot	AI pilot under development to automatically detect and flag relevant events (e.g., human/vessel activity) in surveillance video for informat...	Secteur des programmes	Région de la capitale nationale	Perception et compréhension	Bédard, Mathieu
28	AI-Enabled Electronic Monitoring (EM) Solutions Non traduit	Pilot	The pilot project, in collabration with Fisheries Resources Management, explores the integration of AI-enabled electronic monitoring (EM) so...	Politiques stratégiques	Région de la capitale nationale	Perception et compréhension	Rémillard, Vincent
29	AI-Enabled Detection of Watercraft for Aquatic Invasive Species Risk Management Non traduit	Pilot	This pilot project, in collabration with Aquatic Invasive Species Program, develops an AI-powered system to automatically detect and track...	Politiques stratégiques	Région de la capitale nationale	Perception et compréhension	Spearin, Brendan
32	CTD Anomaly Detection Non traduit	Pilot	The goal of this project is to apply machine learning to detect anomalous oceanographic data, highlighting meaningful physical phenomena in...	Politiques stratégiques	Région de la capitale nationale	Perception et compréhension	Croft, Lee
34	AI-assisted Marine Mammal Annotation Tool Non traduit	Pilot	The goal of the project is to improve the efficiency of the aerial image annotation process to reduce manual efforts of the marine mammal sc...	Politiques stratégiques	Région de la capitale nationale	Perception et compréhension	Wang, Yuchen
36	AI-assisted Solution to summarize C&P aerial surveillance videos Non traduit	Pilot	A pilot project to explore the use of AI/ML for automating the identification of relevant segments in EM video and imagery, such as human ac...	Politiques stratégiques	Région de la capitale nationale	Perception et compréhension	Legresley, Jean-Claude
49	Salmon Fence Counting Non traduit	Pilot	A model using computer vision to analyze water stream fence video-footage, to simultanously count the number of and predict the species of s...	Secteur des programmes	Pacifique	Perception et compréhension	Quan, Eric
50	Salmon Data Digitization (See B22, B26) Non traduit	Pilot	Digitizing non-machine-readable (handwritten) documents and extracts characters into a digital format so that it is accessible, editable, an...	Secteur des programmes	Pacifique	Perception et compréhension	Quan, Eric
52	"Chumputer Vision" (see B32) Salmon Scale Aging Non traduit	Pilot	A model using computer vision to look at images of salmon scale and predict the age of salmon and also monitor its ring growth. CNN and YOL...	Secteur des programmes	Pacifique	Perception et compréhension	Quan, Eric
58	Phytoplankton image classification model Non traduit	Pilot	Create a neural network model to classify phytoplankton images collected by in-situ phytoplankton imaging sensors. There are two discrete s...	Sciences des écosystèmes et des océans	Pacifique	Perception et compréhension	Covert, Paul
64	Automatic detection of unidentified fish sounds Non traduit	Pilot	We used Random Forestes and Convolutional Neural Networks (CNN) algorithms trained on manually detected fish sounds to detect fish sounds co...	Sciences des écosystèmes et des océans	Pacifique	Perception et compréhension	Haggarty, Dana
66	Optical Character Recognition Data transcription tool (See B6, B26) Non traduit	Pilot	Built a proof of concept tool to facilitate data transcription and validation from hand-written field notes. Tested on ~100 year old salmon...	Sciences des écosystèmes et des océans	Pacifique	Perception et compréhension	Bird, Tom
68	Stereo camera image analysis Non traduit	Pilot	Tools built in python to automate image analysis and fish measurement tasks. Applications include a conveyor belt camera and underwater towe...	Sciences des écosystèmes et des océans	Pacifique	Perception et compréhension	Rooper, Chris
74	Pacific Plankton Monitoring: Imagery and automated classification Non traduit	Pilot	• Monitoring of zooplankton and phytoplankton in the water column with shipboard and benchtop plankton imaging instruments (UVP, ZooScan, Pl...	Sciences des écosystèmes et des océans	Pacifique	Perception et compréhension	Sastri, Akash
81	Topaz AI Software Non traduit	Pilot	Enhance underwater video from ROV and tow camera deployments in the offshore region and elsewhere. The deployment of these platforms is incr...	Sciences des écosystèmes et des océans	Pacifique	Perception et compréhension	Best, Merlin
86	Automatic detection of Arctic whales in aerial images Non traduit	Pilot	In collaboration with Dr. David Clausi and his team at the Vision and Image Processing (VIP) Research Lab (University of Waterloo), we have...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
87	ANMPA Habitat Mapping Project Non traduit	Pilot	Drop camera survey to document seafloor habitats and biological communities in the Anguniaqvia niqiqyuam Marine Protected Area (ANMPA) in th...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
88	Development of an AI matching model for bowhead whale photo-identification Non traduit	Pilot	Using DFO’s existing database of bowhead whale photographs, the specific objectives are: 1) Create a machine learning model “detector” for b...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
89	Augmenting Whale Detection in Satellite Images using synthetic data Non traduit	Pilot	An autodetector model was built to detect whales in satellite imagery using synthetic imagery. The primary author is now testing the algorit...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
90	Deep Learning Enhances Beluga Whale Research in the Arctic Non traduit	Pilot	Esri Canada built a deep learning model that automatically detects beluga whales in imagery. The model was developed using satellite, drone...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
91	Localization and Tracking of Beluga Whales in Aerial video Using Deep Learning Non traduit	Pilot	Deep-learning model was developed us YOLOv7 to detect beluga whales from imagery footage. The main contribution of this research is providin...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda
92	Identification automatisée de la condition de carapace de crabe des neiges Non traduit	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...	Sciences des écosystèmes et des océans	Région de la capitale nationale	Perception et compréhension	Winegardner, Amanda

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 Gestion des pêches Perception et compréhension
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 Gestion des pêches Perception et compréhension
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 Dirigeant principal du numérique Perception et compréhension
23
Video Summarization for Aerial Surveillance
AI pilot under development to automatically detect and flag relevant events (e.g., human/vessel activity) in surveillance video for information management. Action plan for this initiative: 1. Governance: Oversight by a multidisciplinary group, routine updates and risk assessments to DM. 2. Guardrails: Pilot with departmental data only, audit outputs for accuracy and bias. 3. Work processes: Deliver targette

Pilot Secteur des programmes Perception et compréhension
28
AI-Enabled Electronic Monitoring (EM) Solutions
The pilot project, in collabration with Fisheries Resources Management, explores the integration of AI-enabled electronic monitoring (EM) solutions to automate the detection and classification of fishing activities using video and sensor data from vessels. This initiative aims to enhance compliance monitoring, reduce manual review time, and support evidence-based fisheries management. Client: Fisheries Resources Management

Pilot Politiques stratégiques Perception et compréhension
29
AI-Enabled Detection of Watercraft for Aquatic Invasive Species Risk Management
This pilot project, in collabration with Aquatic Invasive Species Program, develops an AI-powered system to automatically detect and track watercraft at key entry points to prevent the spread of Aquatic Invasive Species (AIS). By enabling early identification of vessels that may carry invasive organisms, the system supports faster inspections and intervention to protect aquatic ecosystems. Initial exploration using sample data is underway, with the project pending ADM approval for installing cameras and data collection. Client: Aquatic Invasive Species Program

Pilot Politiques stratégiques Perception et compréhension
32
CTD Anomaly Detection
The goal of this project is to apply machine learning to detect anomalous oceanographic data, highlighting meaningful physical phenomena in the state of the ocean for closer investigation by scientists. A proof of conept model has been developed to demonstrate feasibility. Further validation is required with the client to verify alignment with the needs of scientists. Client: Pacific Region - Ocean Sciences Division

Pilot Politiques stratégiques Perception et compréhension
34
AI-assisted Marine Mammal Annotation Tool
The goal of the project is to improve the efficiency of the aerial image annotation process to reduce manual efforts of the marine mammal science team and reduce time-to-insight for marine mammal survey flights. This project is currently in progress. Business requirements have been finalized and development has commenced Client: Ecosystems and Oceans Science - Ecosystems Science Directorate

Pilot Politiques stratégiques Perception et compréhension
36
AI-assisted Solution to summarize C&P aerial surveillance videos
A pilot project to explore the use of AI/ML for automating the identification of relevant segments in EM video and imagery, such as human activity or vessel registration numbers (VRNs). This initiative aims to reduce manual review time and enhance the efficiency of information management in support of fisheries oversight.

Pilot Politiques stratégiques Perception et compréhension
49
Salmon Fence Counting
A model using computer vision to analyze water stream fence video-footage, to simultanously count the number of and predict the species of salmon. YOLO 11 Chinook trained on 735 annotated frames from 63 videos Coho trained on 432 annotated frames from 44 videos Sockeye trained on 1149 annotated frames from 36 videos Chum untrained (annotation of 1368 frames from 43 videos in progress) Pink Humpback

Pilot Secteur des programmes Perception et compréhension
50
Salmon Data Digitization (See B22, B26)
Digitizing non-machine-readable (handwritten) documents and extracts characters into a digital format so that it is accessible, editable, and useable for analysis. Document Intelligence - Custom Classification and Extraction Model Azure OpenAI GPT4o mini Data and Information from ~45,000 pages have been digitized

Pilot Secteur des programmes Perception et compréhension
52
"Chumputer Vision" (see B32) Salmon Scale Aging
A model using computer vision to look at images of salmon scale and predict the age of salmon and also monitor its ring growth. CNN and YOLO 9. Currently trained on 1478 images.

Pilot Secteur des programmes Perception et compréhension
58
Phytoplankton image classification model
Create a neural network model to classify phytoplankton images collected by in-situ phytoplankton imaging sensors. There are two discrete steps to this project: 1) create a library of annotated images for model training; 2) creation and refinement of the CNN model. Convolutional Neural Network, InceptionV3 and ResNet

Pilot Sciences des écosystèmes et des océans Perception et compréhension
64
Automatic detection of unidentified fish sounds
We used Random Forestes and Convolutional Neural Networks (CNN) algorithms trained on manually detected fish sounds to detect fish sounds collected on a passive acoustic monitoring project. The result is an easy-to-use, open-source software called FishSoundFinder, implemented with the CNN detector. Random Forests and CNN

Pilot Sciences des écosystèmes et des océans Perception et compréhension
66
Optical Character Recognition Data transcription tool (See B6, B26)
Built a proof of concept tool to facilitate data transcription and validation from hand-written field notes. Tested on ~100 year old salmon observations and present-day salmon stream inspection logs to determin ethe cost/feasibility/effectiveness of implementing such a tool in Salmon monitoring contexts. (Digitizing non-machine-readable (handwritten) documents and extracts characters into a digital format) Computer vision (azure document intelligence),

Pilot Sciences des écosystèmes et des océans Perception et compréhension
68
Stereo camera image analysis
Tools built in python to automate image analysis and fish measurement tasks. Applications include a conveyor belt camera and underwater towed stereo camera system Computer vision tools (Python, Viame)

Pilot Sciences des écosystèmes et des océans Perception et compréhension
74
Pacific Plankton Monitoring: Imagery and automated classification
• Monitoring of zooplankton and phytoplankton in the water column with shipboard and benchtop plankton imaging instruments (UVP, ZooScan, PlanktoScope). • Application of automated classification of survey and preserved sample image data sets to complement and enhance spatial and temporal resolution of monitoring in Canada’s EEZ and offshore NE Pacific. Development of and regular optimization of automated image classification models using AI: deep learning (residual networks) and machine learning (random forests) methods.

Pilot Sciences des écosystèmes et des océans Perception et compréhension
81
Topaz AI Software
Enhance underwater video from ROV and tow camera deployments in the offshore region and elsewhere. The deployment of these platforms is incredibly time- and resource-consuming, making the optimization of available video an important part of getting the most from this imagery. In addition there are camera limitations from the available DFO survey tools and framegrabs from video will be used as a major product from surveys, making enhancement to high quality images crucial. Topaz Video AI and Topaz Photo AI

Pilot Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension
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 Sciences des écosystèmes et des océans Perception et compréhension