Registre des initiatives en IA

53 initiative(s) courante(s)

• 151

  Zooplankton image analysis

  Use 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

• 104

  Zooplankton classification from Video Plankton Recorder

  Using 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

• 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.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension

• 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

• 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

• 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)

  Pilote Sciences des écosystèmes et des océans Perception et compréhension

• 69

  Species distribution modeling

  Species distribution models for various coral/sponge/groundfish Random Forests

  Pilote Sciences des écosystèmes et des océans Automatisation et aide à la décision

• 189

  Site Assessments

  Automatically 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.

  Idéation En pause Affaires autochtones, aquaculture et gouvernance Perception et compréhension

• 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

• 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

  Pilote Secteur des programmes Perception et compréhension

• 224

  Remote Observation of Watercraft on Roads

  The Aquatic Invasive Species Program is proposing to launch a pilot initiative aimed at addressing a critical knowledge gap in our understanding the spread of aquatic invasive species. Invasive species can attach to boats, trailers, and related equipment—a process commonly referred to as the "stowaway pathway"—which enables their unintentional spread across ecosystems when watercraft are transported between water bodies. The AIS Program is seeking to understand watercraft movement into Canada via international land border crossings. Currently, the extent of this "stowaway" pathway remains largely unquantified. To address this, the pilot will deploy remote camera systems at select border entry points to capture imagery of incoming vehicles. Leveraging machine learning, the system will automatically identify and flag images containing watercraft. This data-driven approach will provide actionable insights to inform strategic decisions on the timing and placement of watercraft inspection and decontamination resources. This initiative may represent a significant step toward enhancing our ability to proactively manage aquatic invasive species risks at key entry points.

  Pilote Perception et compréhension

• 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

  Pilote Sciences des écosystèmes et des océans Perception et compréhension

• 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

• 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

• 37

  OCDS AI Portal

  The 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

• 201

  Maximize detections with AI of the aquatic autonomous environmental DNA (eDNA) sampler

  This Canadian apparatus provides detections of aquatic species through a standardized and reproducible method. Algorithms of AI analyze data from buoy-mounted sensors to determine optimal sampling windows, such as the presence of toxic phytoplankton bloom, and to perform quality assurance on sampling metadata.

  Idéation Automatisation et aide à la décision

• 33

  Machine-learning-assisted Benthic Marine Life Annotation Tool

  The 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

• 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.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension

• 190

  Lobster Stock Assessment

  Automatically 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

• 103

  Improved understanding of the processes impacting harmful algal blooms

  Using 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

• 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

• 246

  GeoAI

  Overview: 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

• 208

  Détection des activités de pêche

  The 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

• 123

  Fish-Habitat Associations

  Extraction of relevant habitat association info and strengths based on vetted literature input to AI for synthesis. Goal to improve equivalency models used in regulatory decisions. Possible partnership with McMaster University.

  Pilote Sciences des écosystèmes et des océans IA cognitive et générative

• 219

  Fish-Habitat Associations

  Extraction of relevant habitat association info and strengths based on vetted literature input to AI for synthesis. Goal to improve equivalency models used in regulatory decisions. Possible partnership with McMaster University.

  Idéation Sciences des écosystèmes et des océans IA cognitive et générative