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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82 initiative(s)

AI initiative registry
ID	AI Initiative	Current Status	Description	Sector	Region	AI Bucket	SME
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	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	Winegardner, Amanda
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	Winegardner, Amanda
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	Winegardner, Amanda
93	Détection de homards et de crabes communs dans les images sous-marines par intelligence artificielle.	Pilot	Développer un processus de capture et d’analyse d’images sous-marine par Intelligence Artificielle pour la détection de homards et de crabes...	Ecosystems and Oceans Science	National Capital Region	Perception and Understanding	Winegardner, Amanda
92	Identification automatisée de la condition de carapace de crabe des neiges	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	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	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	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	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	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	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	Winegardner, Amanda
81	Topaz AI Software	Pilot	Enhance underwater video from ROV and tow camera deployments in the offshore region and elsewhere. The deployment of these platforms is incr...	Ecosystems and Oceans Science	Pacific	Perception and Understanding	Best, Merlin
76	Chumputer Vision - salmon scale age predictive AI (See B8)	Idea	Deep Machine Learning and Convolutional Neural Network application for the predictive application of age assignment of Chum salmon by way of...	Ecosystems and Oceans Science	Pacific	Perception and Understanding	Wischniowski, Stephen
74	Pacific Plankton Monitoring: Imagery and automated classification	Pilot	• Monitoring of zooplankton and phytoplankton in the water column with shipboard and benchtop plankton imaging instruments (UVP, ZooScan, Pl...	Ecosystems and Oceans Science	Pacific	Perception and Understanding	Sastri, Akash
72	Extracting impacts data from regulatory documents	Idea	Use OCR pdf readers, topic detection and language models to detect specific data elements inside written text documents, for extraction and...	Ecosystems and Oceans Science	Pacific	Perception and Understanding	Bird, Tom
68	Stereo camera image analysis	Pilot	Tools built in python to automate image analysis and fish measurement tasks. Applications include a conveyor belt camera and underwater towe...	Ecosystems and Oceans Science	Pacific	Perception and Understanding	Rooper, Chris
66	Optical Character Recognition Data transcription tool (See B6, B26)	Pilot	Built a proof of concept tool to facilitate data transcription and validation from hand-written field notes. Tested on ~100 year old salmon...	Ecosystems and Oceans Science	Pacific	Perception and Understanding	Bird, Tom
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	Haggarty, Dana
62	Deep sea habitat, organism and object detection and identification.	Idea	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	Best, Merlin
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	Covert, Paul
52	"Chumputer Vision" (see B32) Salmon Scale Aging	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...	Programs Sector	Pacific	Perception and Understanding	Quan, Eric
50	Salmon Data Digitization (See B22, B26)	Pilot	Digitizing non-machine-readable (handwritten) documents and extracts characters into a digital format so that it is accessible, editable, an...	Programs Sector	Pacific	Perception and Understanding	Quan, Eric
49	Salmon Fence Counting	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...	Programs Sector	Pacific	Perception and Understanding	Quan, Eric
36	AI-assisted Solution to summarize C&P aerial surveillance videos	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...	Strategic Policy	National Capital Region	Perception and Understanding	Legresley, Jean-Claude

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
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
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
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
93
Détection de homards et de crabes communs dans les images sous-marines par intelligence artificielle.
Développer un processus de capture et d’analyse d’images sous-marine par Intelligence Artificielle pour la détection de homards et de crabes communs.

Pilot Ecosystems and Oceans Science Perception and Understanding
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
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
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
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
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
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
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
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 Ecosystems and Oceans Science Perception and Understanding
76
Chumputer Vision - salmon scale age predictive AI (See B8)
Deep Machine Learning and Convolutional Neural Network application for the predictive application of age assignment of Chum salmon by way of scale images. Chum salmon has been selected as the gateway species due to its relatively easy scale pattern interpretation, this will foster the pathway forward for Chinook, Coho Sockeye and Herring as future targets for AI age interpretation. Deep Machine Learning and Convolutional Neural Network within a Python predictive algorithm

Idea Ecosystems and Oceans Science Perception and Understanding
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 Ecosystems and Oceans Science Perception and Understanding
72
Extracting impacts data from regulatory documents
Use OCR pdf readers, topic detection and language models to detect specific data elements inside written text documents, for extraction and analysis in a cumulative impacts context. Large lanugage models and topic clustering tools

Idea Ecosystems and Oceans Science Perception and Understanding
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 Ecosystems and Oceans Science Perception and Understanding
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 Ecosystems and Oceans Science Perception and Understanding
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
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.

Idea Ecosystems and Oceans Science Perception and Understanding
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
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 Programs Sector Perception and Understanding
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 Programs Sector Perception and Understanding
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 Programs Sector Perception and Understanding
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 Strategic Policy Perception and Understanding