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
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...	Dirigeant principal du numérique	Région de la capitale nationale	IA cognitive et générative	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...	Dirigeant principal du numérique	Région de la capitale nationale	Automatisation et aide à la décision	Bartoli, Saul
5	ITSD knowledge content improvement Non traduit	Pilot	The DFO IT Service Desk is leveraging generative AI (usually Copilot) to enhance its knowledge base articles and Assyst FAQs, which is knowl...	Dirigeant principal du numérique	Québec	IA cognitive et générative	Miousse, Gaétan
22	AI Model for Administrative Support Non traduit	Pilot	Pilot with select staff using MS Copilot for briefing material preparation, data analysis, writing, and translation. Exploring broader acces...	Secteur des programmes	Région de la capitale nationale	IA cognitive et générative	Bédard, Mathieu
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
24	Automated Identification of Vessel Fishing Activity Non traduit	Pilot	Piloting use of vessel AIS data with AI to predict probable fishing activity and provide near real time alerts to officers, aiming to boost...	Secteur des programmes	Région de la capitale nationale	Automatisation et aide à la décision	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
31	Quality control of oceanographic data - Maritimes Region Non traduit	Pilot	The goal of the project is to assist in the processing and quality control of CTD (oceanographic) data by leveraging machine learning models...	Politiques stratégiques	Région de la capitale nationale	Automatisation et aide à la décision	Croft, Lee
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
37	OCDS AI Portal Non traduit	Pilot	The Office of Chief Data Steward (OCDS) currently has in its possession multiple proof-of-concept AI models developed that demonstrates the...	Politiques stratégiques	Région de la capitale nationale	Infrastructure et habilitation	Wang, Yuchen
42	Classification engineered prompts Non traduit	Pilot	Our classification group is testing premade prompts to help with job description writing and analysis. ADDED FROM ID 48: We are exploring...	Ressources humaines et services de gestion	Région de la capitale nationale	IA cognitive et générative	Gardner, Leigh
44	HRMS helpdesk in Power Apps – Power Automate – Sharepoint Non traduit	Pilot	Use AI to determine the correct templatae response to client questions. We would like to use Protected B data (helpdesk requests already ans...	Ressources humaines et services de gestion	Région de la capitale nationale	Automatisation et aide à la décision	Gardner, Leigh
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
51	EN/FR Translation Model Non traduit	Pilot	A large language model that is trained on the Translations Bureau’s and DFO's language-characteristics (i.e. grammar, annotation, syntax, wo...	Secteur des programmes	Pacifique	Interaction et engagement	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
59	Clustering to characterize extreme marine conditions for the benthic region of the Northeastern Pacific continental margin Non traduit	Pilot	We introduce a method for characterizing extremes that uses machine learning to divide the data into regions with relatively consistent envi...	Sciences des écosystèmes et des océans	Pacifique	Automatisation et aide à la décision	Holdsworth, Amber
61	CTD data QC using ML Non traduit	Pilot	Working with CDOS office to streamline our CTD QC using ML cnn and others	Sciences des écosystèmes et des océans	Pacifique	Automatisation et aide à la décision	Guan, Lu
63	Factoid Finder Tool Non traduit	Pilot	The Factoid Finder extracts text from machine-readable PDFs to create searchable content libraries. Small Language Models are used to implem...	—	Pacifique	IA cognitive et générative	Caron, Chantelle
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
65	Comprehensive marine substrate classification applied to Canada’s Pacific shelf Non traduit	Pilot	We built five regional and one coastwide substrate model for the BC coast using substrate observations and seafloor bathymetry derivatives a...	Sciences des écosystèmes et des océans	Pacifique	Automatisation et aide à la décision	Haggarty, Dana

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 Dirigeant principal du numérique IA cognitive et générative
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 Dirigeant principal du numérique Automatisation et aide à la décision
5
ITSD knowledge content improvement
The DFO IT Service Desk is leveraging generative AI (usually Copilot) to enhance its knowledge base articles and Assyst FAQs, which is knowledge intended for clients. This includes creating new knowledge articles, validating and improving existing content, translating materials, and tailoring messaging to different audiences.

Pilot Dirigeant principal du numérique IA cognitive et générative
22
AI Model for Administrative Support
Pilot with select staff using MS Copilot for briefing material preparation, data analysis, writing, and translation. Exploring broader access for process automation. Action plan for this initiative: 1. Governance: Assign project lead, document user access. Regular check-ins with IT and management. 2. Guardrails: Restrict to trained users, apply GC data/privacy standards, conduct ethical reviews and audits. 3. Work pr

Pilot Secteur des programmes IA cognitive et générative
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
24
Automated Identification of Vessel Fishing Activity
Piloting use of vessel AIS data with AI to predict probable fishing activity and provide near real time alerts to officers, aiming to boost compliance coverage and response. Action plan for this initiative: 1. Governance: Supervised under compliance analytics team, reporting to C & P management. 2. Guardrails: Apply strict controls (no personal data), validate accuracy in live pilots, monitoring for unintended use.

Pilot Secteur des programmes Automatisation et aide à la décision
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
31
Quality control of oceanographic data - Maritimes Region
The goal of the project is to assist in the processing and quality control of CTD (oceanographic) data by leveraging machine learning models to predict quality flags for the CTD data products generated by Maritimes region, enabling faster data processing by reducing manual burden. Initial explorations have been conducted to investigate transferability of existing quality control models from Pacific region. Based on investigation results, new models will be trained specific to Maritimes region. Client: Maritimes Region - Ocean Data

Pilot Politiques stratégiques Automatisation et aide à la décision
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
37
OCDS AI Portal
The Office of Chief Data Steward (OCDS) currently has in its possession multiple proof-of-concept AI models developed that demonstrates the potential of AI in fisheries and oceans management. Due to a lack of deployment processes, the models currently sits idle in our repository. This initiative aims to develop a portal where the models can be deployed in a proof-of-concept fashion to enhancement DFO's understanding of AI and drive the cultural change needed to promote the adoption of AI at DFO. The portal has been completed and is accessible at: https://ocds-ai-portal.canadacentral.cloudapp.azure.com/ The portal was shared in the DFO kiosk at the 2025 GC data conference for engagement. It has so far welcomed 360 users, received over 2K views an

Pilot Politiques stratégiques Infrastructure et habilitation
42
Classification engineered prompts
Our classification group is testing premade prompts to help with job description writing and analysis. ADDED FROM ID 48: We are exploring using AI to help with job description writing and editing tasks, and initial tests using copilot have been helpful. It would be nice to have a tool that could write and edit job descriptions, help analyse current job descriptions and help with SoMCs, etc… This wouldn’t need protected data for training, but we would need to upload classification documents needed for this type of work.

Pilot Ressources humaines et services de gestion IA cognitive et générative
44
HRMS helpdesk in Power Apps – Power Automate – Sharepoint
Use AI to determine the correct templatae response to client questions. We would like to use Protected B data (helpdesk requests already answered) as training data.

Pilot Ressources humaines et services de gestion Automatisation et aide à la décision
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
51
EN/FR Translation Model
A large language model that is trained on the Translations Bureau’s and DFO's language-characteristics (i.e. grammar, annotation, syntax, words, scientific term) context. The model’s enables translation to be like those by the Translation Bureau. Training on large database of scientific documents (published by Canadian Science Advisory Secretariat) to understand scientific terminology is currently in progress. MADLAD 400 10B (Initially trained on sample of ~100 translated documents, in progress of additional training with published CSAS documents going back to year 2000)

Pilot Secteur des programmes Interaction et engagement
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
59
Clustering to characterize extreme marine conditions for the benthic region of the Northeastern Pacific continental margin
We introduce a method for characterizing extremes that uses machine learning to divide the data into regions with relatively consistent environmental conditions (temperature, oxygen, acidity), and define the extremes based on the historical statistics of variability of each of these fields. unsupervised clustering with k-means

Pilot Sciences des écosystèmes et des océans Automatisation et aide à la décision
61
CTD data QC using ML
Working with CDOS office to streamline our CTD QC using ML cnn and others

Pilot Sciences des écosystèmes et des océans Automatisation et aide à la décision
63
Factoid Finder Tool
The Factoid Finder extracts text from machine-readable PDFs to create searchable content libraries. Small Language Models are used to implement semantic search techniques, allowing users to search the library for relevant passages within the PDFs. Small Language Model and Text Embedder: MS Marco Distilbert Dot-v5 Cross-encoder: MS Marco MiniLM-L6-v2

Pilot IA cognitive et générative
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
65
Comprehensive marine substrate classification applied to Canada’s Pacific shelf
We built five regional and one coastwide substrate model for the BC coast using substrate observations and seafloor bathymetry derivatives and oceanographic predictors using Random Forests. Random Forests

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