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.
Filter 1
63 current initiative(s)
-
192
Harbour AssessmentsAutomatically and continuously assess harbour conditions via drone and satellite imagery Solution being developed for Small Craft harbours to assess harbour conditions (presence of ice, usage, vessel and vehicle traffic, change detection) based on drone footage and satellite imagery.
Ideation On hold Indigenous Affairs, Aquaculture and Governance Perception and Understanding Computer Vision
-
191
Irish Moss ResearchAutomatically and non-invasively detect and assess health of Irish Moss Solution being developed for Science to review hundreds of hours of video for Irish Moss presence and health.
Ideation On hold Indigenous Affairs, Aquaculture and Governance Perception and Understanding Computer Vision
-
189
Site AssessmentsAutomatically assess presence of eel grass from drone video Solution being developped to assist Fish and Fish Habitat Protection Program in reviewing hundreds of hours of drone footage to detect presence of Eel Grass.
Ideation On hold Indigenous Affairs, Aquaculture and Governance Perception and Understanding Computer Vision
-
162
Recherche et développement d’outils de vision automatisée pour l’étude des refuges marins des coraux et des éponges du golfe du Saint-Laurent.Projet de contribution avec le Centre de développement et de recherche en intelligence numérique (CDRIN). L’objectif est de concevoir et développer un outil d’intelligence artificielle permettant de détecter et suivre les organismes benthiques filmés dans refuges marins des coraux et des éponges du Golfe du Saint-Laurent.
Ideation Ecosystems and Oceans Science Perception and Understanding Computer Vision
-
174
AI model for Video reviewAn AI model that reviews video monitoring footage for a fishery or given vessel and machine learns potential events of non compliance
Ideation Programs Sector Perception and Understanding Computer Vision
-
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
Ideation Ecosystems and Oceans Science Perception and Understanding Computer Vision
-
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
-
33
Machine-learning-assisted Benthic Marine Life Annotation ToolThe goal of the project is to improve the efficiency of the image annotation process to reduce manual efforts of the benthic science team and reduce time-to-insight from underwater surveys. Further development is prevented by DFO's IT infrastructure. There are ongoing discussions with CDOS to resolved this but the project is on hold in the meantime. Client: Quebec Region/Pacific Region - Oceans and Ecosystem Sciences Division
Ideation On hold Strategic Policy Perception and Understanding Computer Vision
-
35
AI-assisted Solution to Detect Ghost Gear from Side Scan Sonar ImagesThe goal of the project is use AI to automate data analysis in detecting Ghost Gear. Client: Ghost Gear Program
Ideation On hold Strategic Policy Perception and Understanding Computer Vision
-
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.
Development Fisheries Management Perception and Understanding Computer Vision
-
228
SILScannerDeploy on DFO's PBMM Cloud a proof of concept application that uses optical character recognition (OCR) to digitize and validate Stream Inspection Logs (SILs). The PoC was originally developed on SSC's cloud infrastructure. It is now being deployed in DFO PB cloud infrastructure. BLADES initiative and IT security teams are involved.
Pilot Chief Digital Officer Perception and Understanding Computer Vision
-
190
Lobster Stock AssessmentAutomatically assess lobster count and habitat from hours of video efficiently Solution being developed for Science to review hundreds of hours of video for lobster count and state of habitat.
Pilot Indigenous Affairs, Aquaculture and Governance Perception and Understanding Computer Vision
-
213
Numérisation de documents manuscritsEn collaboration avec l’équipe nationale de l’Intendant principal des données (PSSI), visant principalement la pêche au saumon. L’objectif est de numériser des documents avec des données manuscrites et de les faire analyser par un modèle d’intelligence artificielle qui va décoder l’écriture pour la transformer en données numériques et les insérer dans une base de données. Le but est de remplacer la saisie manuelle de ces données.
Pilot Statistics, Policy and Licensing Perception and Understanding Computer Vision
-
187
Atlantic Bluefin Tuna MonitoringAutomatically perform video analytics from hundred of harvester tuna video Solution being developed in region to assist C&P in batch processing of at-sea video monitoring footage.
Pilot Indigenous Affairs, Aquaculture and Governance Perception and Understanding Computer Vision
-
212
Surveillance électronique en merIl s’agit de capture d’images vidéo sur les navires de pêche pour ensuite les faire analyser par des ressources humaines et un modèle d’intelligence artificielle doté de capacité d’identification d’événements, d’espèces, etc. Projet fait en collaboration avec l’équipe nationale de l’Intendant principal des données.
Pilot Statistics, Policy and Licensing Perception and Understanding Computer Vision
-
217
Suite du projet de recherche de recherche et développement d’outils de vision automatisée pour l’étude des refuges marins des coraux et des éponges du golfe du Saint-Laurent.Une entente de contribution pour l’année 2025-2026 (Programme de gestion des océans) visant à l’amélioration de l’outil développé par le Centre de développement et de recherche en intelligence numérique (CDRIN) en 2024-2025. Le projet a comme objectif global de concevoir et développer un outil d’intelligence artificielle permettant de détecter et suivre les organismes benthiques filmés dans les refuges marins des coraux et des éponges du Golfe du Saint-Laurent.
Pilot Marine Planning and Conservation Perception and Understanding Computer Vision
-
218
L’observation des bélugas de l’estuaire du Saint-LaurentEntente de contribution avec le Groupe de recherche et d'éducation sur les mammifères marins (GREMM) pour le développement d'un module de mesures morphométriques assistées par l’intelligence artificielle pour les bélugas de l'estuaire du Saint-Laurent L’entente s’est terminée le 31 mars 2024 mais le module est en cours d’utilisation. Considérant la contrainte de temps imposée par les mesures manuelles des images prises par drones de bélugas, l’équipe de recherche du GREMM a développé une méthode exploitant l’IA pour mesurer plus rapidement et plus efficacement les bélugas.
Pilot Marine Planning and Conservation Perception and Understanding Computer Vision
-
188
Coastal SurveillanceAutomatically process thousands of drone images as part of continuous surveillance Solution being developed in region to assist C&P in batch processing of drone surveillance images.
Pilot Indigenous Affairs, Aquaculture and Governance Perception and Understanding Computer Vision
-
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 Computer Vision
-
94
DFO Bot, deep learning application to analyse otolith images available in DFO DotsDevelopment 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 Computer Vision
-
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
-
100
Development of AI technology for otolith and scale ageingAgeing 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
-
103
Improved understanding of the processes impacting harmful algal bloomsUsing 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
-
96
Use of machine-learning (ML) algorithms and high-performance computing (HPC) for the optimization of satellite image analysis for mapping coastal habitatsThis 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