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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198 initiative(s) courante(s)
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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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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99
Improved freshwater runoff modelling in Eastern Canada to drive ocean modelsUse 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.
Pilote Sciences des écosystèmes et des océans Automatisation et aide à la décision
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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.
Pilote Sciences des écosystèmes et des océans Automatisation et aide à la décision
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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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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92
Identification automatisée de la condition de carapace de crabe des neigesDétermination 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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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91
Localization and Tracking of Beluga Whales in Aerial video Using Deep LearningDeep-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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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90
Deep Learning Enhances Beluga Whale Research in the ArcticEsri 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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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89
Augmenting Whale Detection in Satellite Images using synthetic dataAn 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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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88
Development of an AI matching model for bowhead whale photo-identificationUsing 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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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87
ANMPA Habitat Mapping ProjectDrop 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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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86
Automatic detection of Arctic whales in aerial imagesIn 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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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85
Daily UseBing CoPilot, ChatGPT-4 Emails, documents, speaking points, ideas
Pilote Secteur des programmes IA cognitive et générative
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84
Daily UseDashboard Development, Used the standalone M365 version of Copilot to assist with coding.
Pilote Secteur des programmes IA cognitive et générative
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83
Daily UseMapping, The AI Assist function in FME has been used to support code development for data processing while integrating and manipulating data in workbenches.
Pilote Secteur des programmes Automatisation et aide à la décision
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81
Topaz AI SoftwareEnhance 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
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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80
Various Built-In AI Features (Co-pilot, ChatGPT, etc)Using built-in AI features and functions in regular workflow operations. This includes LLM's such as ChatGPT, Microsoft Copilot, Gemini, and various others that are integrated directly into the applications already currently being used. Large Language Models
Pilote Sciences des écosystèmes et des océans IA cognitive et générative
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78
Okanagan sockeye life cycle modelUsing LLM (ChatGPT) to support the development of a statistical lifecycle model for Okanagan sockeye. LLMs are not developing the models but are used to brainstorm modelling approaches, debug code, and confirm that written model descriptions in manuscripts are accurate and clear. AI is not being tasked with developing these models. Large Language Models
Pilote Sciences des écosystèmes et des océans IA cognitive et générative
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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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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70
Data Validation Tool (See B6, B22)Use large language models to help detect errors in data transcribed using optical character recognition. Apply validation rules to propose corrections and normalize data into a standardized format. (Digitizing non-machine-readable (handwritten) documents and extracts characters into a digital format) Llama 3.0 Large Language Model
Pilote Sciences des écosystèmes et des océans Automatisation et aide à la décision
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69
Species distribution modelingSpecies distribution models for various coral/sponge/groundfish Random Forests
Pilote Sciences des écosystèmes et des océans Automatisation et aide à la décision
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68
Stereo camera image analysisTools 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)
Pilote Sciences des écosystèmes et des océans Perception et compréhension