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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225
Innovation en matière de données – Initiative relative à la stratégie sur le saumon du PacifiqueDiscover innovative data and artificial intelligence (A.I) projects addressing complex salmon data challenges made possible through strategic investments from the Pacific Salmon Strategy Initiative (PSSI). PSSI initiatives harness the power of advanced data and A.I. technologies like machine learning, computer vision, and natural language processing to revolutionize how to support salmon conservation and rebuilding efforts in the modern data and digital era. By integrating advanced data and A.I. technologies with program activities such as habitat restoration, ecosystem planning, sustainable fishery management practices, and collaborative efforts in Science, the PSSI leverages modern tools to enhance planning and data-driven decision-making. Together, these data and A.I. efforts create high quality and more accessible data and enable better insights and actions to protect and restore Pacific salmon and its ecosystems.
Idéation Infrastructure et habilitation
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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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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
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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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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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64
Automatic detection of unidentified fish soundsWe 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
Pilote Sciences des écosystèmes et des océans Perception et compréhension
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36
AI-assisted Solution to summarize C&P aerial surveillance videosA 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.
Pilote Politiques stratégiques Perception et compréhension
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34
AI-assisted Marine Mammal Annotation ToolThe goal of the project is to improve the efficiency of the aerial image annotation process to reduce manual efforts of the marine mammal science team and reduce time-to-insight for marine mammal survey flights. This project is currently in progress. Business requirements have been finalized and development has commenced Client: Ecosystems and Oceans Science - Ecosystems Science Directorate
Pilote Politiques stratégiques Perception et compréhension
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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
Idéation En pause Politiques stratégiques Perception et compréhension
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29
AI-Enabled Detection of Watercraft for Aquatic Invasive Species Risk ManagementThis pilot project, in collabration with Aquatic Invasive Species Program, develops an AI-powered system to automatically detect and track watercraft at key entry points to prevent the spread of Aquatic Invasive Species (AIS). By enabling early identification of vessels that may carry invasive organisms, the system supports faster inspections and intervention to protect aquatic ecosystems. Initial exploration using sample data is underway, with the project pending ADM approval for installing cameras and data collection. Client: Aquatic Invasive Species Program
Pilote Politiques stratégiques Perception et compréhension
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AI-Enabled Electronic Monitoring (EM) SolutionsThe pilot project, in collabration with Fisheries Resources Management, explores the integration of AI-enabled electronic monitoring (EM) solutions to automate the detection and classification of fishing activities using video and sensor data from vessels. This initiative aims to enhance compliance monitoring, reduce manual review time, and support evidence-based fisheries management. Client: Fisheries Resources Management
Pilote Politiques stratégiques Perception et compréhension
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246
GeoAIOverview: Potential GeoAI‑Impacted Programs and Processes GeoAI (the integration of geospatial data, AI, and machine learning) is being explored to enhance decision‑making, efficiency, and service delivery by automating spatial analysis, identifying patterns at scale, and enabling predictive insights. 1. Program and Service Delivery GeoAI can support more targeted, timely, and evidence‑based services by: Identifying spatial patterns in environmental, socio‑economic, or ecological data Prioritizing areas for intervention, monitoring, or investment Improving accessibility and equity of services through location‑based insights Examples Risk‑based prioritization of inspections, monitoring sites, or enforcement activities Predictive habitat or species distribution mapping to support conservation or fisheries management Enhanced situational awareness for emergency response or environmental incidents 2. Operational Efficiency GeoAI enables automation and scalability in routine or data‑intensive operations, reducing manual effort and turnaround times. Examples Automated feature extraction from satellite imagery, aerial photos, or LiDAR (e.g., shoreline change, infrastructure, habitat types) Near‑real‑time monitoring of environmental conditions or operational assets Change detection to flag anomalies or emerging risks Operational Benefits Reduced reliance on manual digitization and visual interpretation Faster updates to spatial products and dashboards More consistent and repeatable analytical outputs 3. Business Processes and Decision Support GeoAI can modernize internal business processes by embedding spatial intelligence into planning, reporting, and governance workflows. Examples Predictive analytics to support long‑term planning and scenario analysis Decision‑support tools that integrate geospatial, operational, and administrative data AI‑assisted data quality assessment and metadata generation Process Impacts Improved transparency and defensibility of decisions Better integration of spatial data across programs and systems Enhanced performance measurement and outcome tracking 4. Cross‑Cutting Opportunities Across all domains, GeoAI supports: Data integration: Linking spatial, temporal, and non‑spatial datasets Scalability: Applying consistent analysis across large geographic areas Innovation: Enabling new analytical questions that were previously impractical
Idéation Dirigeant principal du numérique Automatisation et aide à la décision
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OCDS AI PortalThe Office of Chief Data Steward (OCDS) currently has in its possession multiple proof-of-concept AI models developed that demonstrates the potential of AI in fisheries and oceans management. Due to a lack of deployment processes, the models currently sits idle in our repository. This initiative aims to develop a portal where the models can be deployed in a proof-of-concept fashion to enhancement DFO's understanding of AI and drive the cultural change needed to promote the adoption of AI at DFO. The portal has been completed and is accessible at: https://ocds-ai-portal.canadacentral.cloudapp.azure.com/ The portal was shared in the DFO kiosk at the 2025 GC data conference for engagement. It has so far welcomed 360 users, received over 2K views an
Pilote Politiques stratégiques Infrastructure et habilitation
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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
Idéation 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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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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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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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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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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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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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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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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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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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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Automated Identification of Vessel Fishing ActivityPiloting use of vessel AIS data with AI to predict probable fishing activity and provide near real time alerts to officers, aiming to boost compliance coverage and response. Action plan for this initiative: 1. Governance: Supervised under compliance analytics team, reporting to C & P management. 2. Guardrails: Apply strict controls (no personal data), validate accuracy in live pilots, monitoring for unintended use.
Pilote Secteur des programmes Automatisation et aide à la décision