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.
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198 current initiative(s)
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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
Pilot Strategic Policy Perception and Understanding
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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
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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.
Pilot Strategic Policy Perception and Understanding
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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
Pilot Strategic Policy Perception and Understanding
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28
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
Pilot Strategic Policy Perception and Understanding
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256
AI-Enabled UX Design and Content WorkflowsAI-enabled capabilities to help UX and content designers deliver digital work faster and more consistently. This includes supporting early ideas, rapid iteration, drafting and revising content, creating and comparing design options (task flows, wireframes, mockups, interactions, prototypes), and producing documentation needed for handoffs and approvals while still meeting GC standards. We also want employees to build modern, transferable skills.
Development Chief Digital Officer
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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
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176
Analysis of rolesAnalysis of roles: Ad hoc analysis of roles within C&P, comparing 4 different roles and their responsibilities.
Ideation Strategic Policy Automation and Decision Support
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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.
Pilot Ecosystems and Oceans Science Perception and Understanding
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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
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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.
Pilot Ecosystems and Oceans Science Perception and Understanding
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127
Automated assessment of Crowd Source BathymetryThe Community Hydrography team is working with the University of New Brunswick (UNB) on a tool-kit that will automate the assessment of large volumes of crowd source data. AI routines will allow for automated QC, and drafting of Navigation Warnings.
Pilot Ecosystems and Oceans Science Automation and Decision Support
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222
Automated assessment of Crowd Source BathymetryThe Community Hydrography team is working with the University of New Brunswick (UNB) on a tool-kit that will automate the assessment of large volumes of crowd source data. AI routines will allow for automated QC, and drafting of Navigation Warnings.
Pilot Ecosystems and Oceans Science Automation and Decision Support
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126
Automated assessment of satellite imageryAI routines are being developed and tested to assess large volume of satellite imagery and data to: i) Automatically Identify marine hazards and ii) Change detection – i.e. shorelines or riverbank changes.
Pilot Ecosystems and Oceans Science Perception and Understanding
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223
Automated assessment of satellite imageryAI routines are being developed and tested to assess large volume of satellite imagery and data to: i) Automatically Identify marine hazards and ii) Change detection – i.e. shorelines or riverbank changes.
Pilot Ecosystems and Oceans Science Perception and Understanding
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194
Automated ELOG reportingAutomatically generate daily insights and reports on use of ELOGs Solution being developed to generate real-time reporting of ELOG use and implementation.
Pilot Indigenous Affairs, Aquaculture and Governance Automation and Decision Support
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24
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.
Pilot Programs Sector Automation and Decision Support
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125
Automated Paper Chart SolutionCHS is working closely with software provider CARIS to test and implement automated solution to generate Paper Charts from digital data sources. AI routines will improve speed and accuracy of solutions (machine learning) as software is refined to ensure product specifications are respected (as they are used for official navigation).
Pilot Ecosystems and Oceans Science Automation and Decision Support
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221
Automated Paper Chart SolutionCHS is working closely with software provider CARIS to test and implement automated solution to generate Paper Charts from digital data sources. AI routines will improve speed and accuracy of solutions (machine learning) as software is refined to ensure product specifications are respected (as they are used for official navigation).
Pilot Ecosystems and Oceans Science Automation and Decision Support
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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.
Pilot Ecosystems and Oceans Science Perception and Understanding
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145
Automatic detection of unidentified fish soundsThis completed project used Random Forests 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.
Pilot Ecosystems and Oceans Science Perception and Understanding
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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
Pilot Ecosystems and Oceans Science Perception and Understanding
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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.
Pilot Indigenous Affairs, Aquaculture and Governance Automation and Decision Support
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182
Basic Translations & Data ProcessingWe have identified several effective use cases: Processing machine readable data into human readable formats, basic translations, and summarizing large documents and reports into digestible formats. We participated in tests of the DFO Pilot ChatGPT (GPT3) which was available prior to the DFO implementation of MS Copilot. As part of our testing, we identified several effective use cases which apply to both options since they're based on the same under
Ideation Programs Sector Cognitive and Generative AI
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116
BinderBot: A Smart Planning BinderEmploys AI-driven extraction to identify and compile the most relevant data from technical documents for the planning binder. Streamlines the planning binder process by automatically assembling targeted, information ready for executive review. Saves valuable administrative time and ensures that binder materials are both comprehensive and user-friendly.
Pilot Ecosystems and Oceans Science Automation and Decision Support