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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53 initiative(s) courante(s)
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208
Détection des activités de pêcheThe goal is to use vessel movement behaviour in the form of AIS data to predict whether a vessel is currently engaged in fishing activity. AIS data is available to the department in near real-time from AIS transponders equipped on vessels. An automated system that can indicate to fishery officers when and where vessels are likely engaged in fishing activities will support better monitoring of compliance with regulations such as conformance with conditions in fishing licenses and in marine protected areas. Due to limited capacity and lengthy processes to manually review and distill data sources, fishery officers cannot fully monitor all vessels under present circumstances. This AI-supported system would help to increase monitoring coverage for fishery officers. Spatial processing: The vast amounts of geographically-referenced AIS transmissions from vessels must be refined to spatiotemporal areas of interest prior to feature engineering for input to the machine learning model. Appropriate spatial processing can support achieving this in a timely manner, particularly when near real-time processing is of interest. However, given the availability of the AIS pipeline, this processing is more appropriate to be performed by the pipeline than within the analytics environment for this use case. The fishing detection system would use the AIS pipeline API and would received data which has already been refine to the relevant spatial extents.
Pilote Politiques stratégiques Automatisation et aide à la décision
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193
Autonomous Task TrackerAutonomously complete tasks for fishery officer with multi-agent AI. Solution being developed in region to assist C&P autonomous completion of administrative tasks.
Pilote Affaires autochtones, aquaculture et gouvernance Automatisation et aide à la décision
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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.
Pilote Affaires autochtones, aquaculture et gouvernance Perception et compréhension
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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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121
Develop a deep-learning model to age capelin otoliths: application for stock assessmentInvestigate the utility of applying a deep-learning model developed by the DFO Gulf Region for aging fish otoliths; specifically, to determine the age of capelin in the 2J3KL stock in Newfoundland for use in stock assessments. Otolith aging is currently done visually using a microscope, this proposed method would automate that process to save time and resources, while reducing human error and bias
Pilote Sciences des écosystèmes et des océans Automatisation et aide à la décision
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168
Development of bespoke data-limited tools using AI coding.The majority of Canadian fish stocks are data limited thus preventing full analytical stock assessments. Data limited tools are therefore the only approach. We are engaged in quick Ai development of custom data limited assessment approaches for exploited fish stocks
Pilote Sciences des écosystèmes et des océans IA cognitive et générative
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151
Zooplankton image analysisUse Random Forest and residual neural networks (ResNet) for automated classification of zooplankton images collected using: 1) scanning of preserved samples (ZooScan); and 2) in situ imagery (Underwater Vision Profiler, UVP). Imagery focused on surveys and samples collected along the West Coast of Vancouver Island and offshore from 2022-present.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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169
Dark Vessel Detection PlatformUtilizes AI and machine learning to detect vessels in satellite imagery, predict movements, and flag potential IUU fishing globally.Automatically cross-references detected vessels with AIS and VMS data to identify unregistered or "dark" vessels. Supports international IUU fishing monitoring; new AI-driven features enable vessel identification from electro-optical imagery. Governance: Managed by DFO’s International Fisheries Enforcement team; technical oversight by MDA Space; coordinated with CSA and other OGDs. Guardrails: All data use complies with commercial licensing terms and GC privacy requirements; sharing restricted
Pilote Secteur des programmes Perception et compréhension
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118
Use of artificial intelligence models to facilitate seafloor video annotations in conservation areas.The purpose of this initiative is to utilize AI to facilitate seafloor video annotations collected as part of the MCT and Benthic Ecology programs in the NL Region. A large number of vidEcosystems and Oceans Science is collected annually, and manual video annotation (e.g., locating and counting observations) is extremely time-consuming. A published “object detection model” (FathomNet Megalodon Detector, YOLOv8x) trained using marine taxa by MBARI is currently being used to identify objects in our seafloor images. The model does not identify the objects (e.g., to species), but it largely accelerates the process given that locating objects is the most time-consuming part of the work.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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108
The development of rapid multiplex qPCR for the detection and quantification of three parasites MSX, SSO, Dermo of oysters (Crassostrea virginica) and MSX single-cell whole genome sequencing using long read sequencing platform’This 2-year project aims to enhance the disease diagnostic services provided to the shellfish aquaculture industry by improving the methods for detecting economically important Oyster parasites, enhance understanding of Multinucleate Sphere Unknown X (MSX) virulence dynamics, and support the creation of new vaccine in investigating future outbreaks and modeling through machine learning.
Pilote Sciences des écosystèmes et des océans Automatisation et aide à la décision
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106
Finwave: an online photo-identification database and AI matching toolThis project aims to improve a publicly accessible machine learning platform capable of identifying individual killer whales in real-time from submitted photographs. The ultimate goal is to improve the web-based interface and validate the algorithm used for photo identification against known individuals.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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105
Combining artificial intelligence and drone imagery for automatic extraction of morphometric attributes of small cetaceans: Application to the St. Lawrence Estuary Beluga population.This project proposes to use a multiyear time series of drone images of the St. Lawrence Estuary Beluga population to develop a series of algorithms for automatic extraction of morphometric information of individual beluga whales using AI.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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104
Zooplankton classification from Video Plankton RecorderUsing 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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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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.
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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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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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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122
Development and testing of artificial intelligence and machine learning to analyze underwater photo and video data to assess marine impacts and resourcesThis completed project (2020/21) aimed to utilize machine learning and imaging technology in fisheries science. The proponent planned to use Video and Image Analytics for Marine Environments (VIAME), an open-source system developed by NOAA, for two specific purposes: 1. Measure the movement of Atlantic cod across boundaries at the Gilbert Bay Marine Protected Area in Newfoundland and Labrador. 2. Assess the impact of seismic surveying on Atlantic cod as part of a project funded by the Environmental Studies Research Fund (ESRF). The automatic detection and count of fish near MPA boundaries was expected to reduce use of personnel time and effort.
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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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