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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197 current initiative(s)

AI initiative registry
ID	AI Initiative	Stage	Description	Sector	Region	AI Bucket	SME
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
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
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
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
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
91	Localization and Tracking of Beluga Whales in Aerial video Using Deep Learning	Pilot	Deep-learning model was developed us YOLOv7 to detect beluga whales from imagery footage. The main contribution of this research is providin...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding Computer Vision	Winegardner, Amanda
84	Daily Use	Pilot	Dashboard Development, Used the standalone M365 version of Copilot to assist with coding.	Programs Sector	National Capital Region	Cognitive and Generative AI Generative AI (text, images, code)	Newman, Candace
95	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	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
98	Guidance on Optimal Timing for Environmental DNA (GOTeDNA)	Pilot	Provide guidance on optimal eDNA sampling periods and standardized sampling procedures for assessing and monitoring coastal species using eD...	Ecosystems and Oceans Science	National Capital Region	Automation and Decision Support Prescriptive Analytics	Winegardner, Amanda
99	Improved freshwater runoff modelling in Eastern Canada to drive ocean models	Pilot	Use Neural Networks to post-process streamflow simulations from the WRF-Hydro model, improving their agreement with observational data. Thes...	Ecosystems and Oceans Science	National Capital Region	Automation and Decision Support Predictive Analytics	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
101	Creating space-time continuous surface chlorophyll fields for northwest Atlantic using neural network methods	Pilot	Neural network methods are applied to fill the space-time gaps in satellite remote sensing data of surface chlorophyll for the Northwest Atl...	Ecosystems and Oceans Science	National Capital Region	Cognitive and Generative AI Predictive Analytics	Winegardner, Amanda
102	Past, Present and Future Oceanographic Conditions in Canada's MPAs and Marine OECMs (Other Effective area-based Conservation Measures)	Pilot	Downscaling climate models using neural networks for conservation areas. Neural networks allow us to represent coarse resolution climate pro...	Ecosystems and Oceans Science	National Capital Region	Cognitive and Generative AI Predictive Analytics	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
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
69	Species distribution modeling	Pilot	Species distribution models for various coral/sponge/groundfish Random Forests	Ecosystems and Oceans Science	Pacific	Automation and Decision Support Predictive Analytics	Rooper, Chris
58	Phytoplankton image classification model	Pilot	Create a neural network model to classify phytoplankton images collected by in-situ phytoplankton imaging sensors. There are two discrete s...	Ecosystems and Oceans Science	Pacific	Perception and Understanding Computer Vision	Covert, Paul
59	Clustering to characterize extreme marine conditions for the benthic region of the Northeastern Pacific continental margin	Pilot	We introduce a method for characterizing extremes that uses machine learning to divide the data into regions with relatively consistent envi...	Ecosystems and Oceans Science	Pacific	Automation and Decision Support Predictive Analytics	Holdsworth, Amber
61	CTD data QC using ML	Pilot	Working with CDOS office to streamline our CTD QC using ML cnn and others	Ecosystems and Oceans Science	Pacific	Automation and Decision Support Predictive Analytics	Guan, Lu
62	Deep sea habitat, organism and object detection and identification.	Ideation	Proposing a capstone project to Computer Science students from Camosun College to develop a pipeline for identifying objects and loading the...	Ecosystems and Oceans Science	Pacific	Perception and Understanding Computer Vision	Best, Merlin
63	Factoid Finder Tool	Pilot	The Factoid Finder extracts text from machine-readable PDFs to create searchable content libraries. Small Language Models are used to implem...	—	Pacific	Cognitive and Generative AI Knowledge Management	Caron, Chantelle
64	Automatic detection of unidentified fish sounds	Pilot	We used Random Forestes and Convolutional Neural Networks (CNN) algorithms trained on manually detected fish sounds to detect fish sounds co...	Ecosystems and Oceans Science	Pacific	Perception and Understanding Audio Event Detection	Haggarty, Dana
65	Comprehensive marine substrate classification applied to Canada’s Pacific shelf	Pilot	We built five regional and one coastwide substrate model for the BC coast using substrate observations and seafloor bathymetry derivatives a...	Ecosystems and Oceans Science	Pacific	Automation and Decision Support Predictive Analytics	Haggarty, Dana

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
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
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
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
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
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 Ecosystems and Oceans Science Perception and Understanding Computer Vision
84
Daily Use
Dashboard Development, Used the standalone M365 version of Copilot to assist with coding.

Pilot Programs Sector Cognitive and Generative AI Generative AI (text, images, code)
95
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 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
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 Ecosystems and Oceans Science Automation and Decision Support Prescriptive Analytics
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 Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
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
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 Ecosystems and Oceans Science Cognitive and Generative AI Predictive Analytics
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 Ecosystems and Oceans Science Cognitive and Generative AI Predictive Analytics
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
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
69
Species distribution modeling
Species distribution models for various coral/sponge/groundfish Random Forests

Pilot Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
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 Ecosystems and Oceans Science Perception and Understanding Computer Vision
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 Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
61
CTD data QC using ML
Working with CDOS office to streamline our CTD QC using ML cnn and others

Pilot Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
62
Deep sea habitat, organism and object detection and identification.
Proposing a capstone project to Computer Science students from Camosun College to develop a pipeline for identifying objects and loading the results into Biigle for human review. TBD.

Ideation Ecosystems and Oceans Science Perception and Understanding Computer Vision
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 Cognitive and Generative AI Knowledge Management
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 Ecosystems and Oceans Science Perception and Understanding Audio Event Detection
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 Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics