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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21 current initiative(s)

AI initiative registry
ID	AI Initiative	Stage	Description	Sector	Region	AI Bucket	SME
248	Analyse des tracés AIS en mer Not translated · original: French	Pilot	Analyse des tracés AIS en utilisant l'IA: un IA a été créé pour analyser les tracés de bateau et déterminer si des activités de pêche ont li...	Conservation and Protection	Quebec	Automation and Decision Support Predictive Analytics	Beauchamp, Sébastien
175	AI model for duplcate or dubious licence accounts	Ideation	Licencing systems hold 10 times the amount accounts compared to licences issued. Many duplicate and dubious accounts across the millions of...	Programs Sector	National Capital Region	Automation and Decision Support Predictive Analytics	Bédard, Mathieu
24	Automated Identification of Vessel Fishing Activity	Pilot	Piloting use of vessel AIS data with AI to predict probable fishing activity and provide near real time alerts to officers, aiming to boost...	Programs Sector	National Capital Region	Automation and Decision Support Predictive Analytics	Bédard, Mathieu
120	Characterizing areas of convergence in the Saint John Harbour	Pilot	This project is using a technique called self-organizing maps, which is an unsupervised machine learning algorithm, to identify patterns in...	Ecosystems and Oceans Science	National Capital Region	Automation and Decision Support Predictive Analytics	Winegardner, Amanda
128	CICADA (cumulative effects spatial data tool) Phase 2: determining stressor effects and interactions to inform decision-making	Pilot	Geospatial and machine learning techniques will be applied to determine the overall effect, stress(or) interactions, and relative influences...	Ecosystems and Oceans Science	National Capital Region	Automation and Decision Support Predictive Analytics	Winegardner, Amanda
59	Clustering to characterize extreme marine conditions for the benthic region of the Northeastern Pacific continental margin	Pilot	We introduce a method for characterizing extremes that uses machine learning to divide the data into regions with relatively consistent envi...	Ecosystems and Oceans Science	Pacific	Automation and Decision Support Predictive Analytics	Holdsworth, Amber
65	Comprehensive marine substrate classification applied to Canada’s Pacific shelf	Pilot	We built five regional and one coastwide substrate model for the BC coast using substrate observations and seafloor bathymetry derivatives a...	Ecosystems and Oceans Science	Pacific	Automation and Decision Support Predictive Analytics	Haggarty, Dana
101	Creating space-time continuous surface chlorophyll fields for northwest Atlantic using neural network methods	Pilot	Neural network methods are applied to fill the space-time gaps in satellite remote sensing data of surface chlorophyll for the Northwest Atl...	Ecosystems and Oceans Science	National Capital Region	Cognitive and Generative AI Predictive Analytics	Winegardner, Amanda
32	CTD Anomaly Detection	Pilot	The goal of this project is to apply machine learning to detect anomalous oceanographic data, highlighting meaningful physical phenomena in...	Strategic Policy	National Capital Region	Perception and Understanding Predictive Analytics	Croft, Lee
61	CTD data QC using ML	Pilot	Working with CDOS office to streamline our CTD QC using ML cnn and others	Ecosystems and Oceans Science	Pacific	Automation and Decision Support Predictive Analytics	Guan, Lu
41	Cyber Security Operation	Production	Cyber Security Operation Centre has deployed AI/ML in MS Defender (Endpoint Detection and Response (EDR) system), MS Sentinel (Security Inci...	Chief Digital Officer	National Capital Region	Automation and Decision Support Predictive Analytics	Jensen, Michael
208	Fishing Detection	Pilot	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. A...	Strategic Policy	National Capital Region	Automation and Decision Support Predictive Analytics	Croft, Lee
246	GeoAI	Ideation	Overview: Potential GeoAI‑Impacted Programs and Processes GeoAI (the integration of geospatial data, AI, and machine learning) is being exp...	Chief Digital Officer	Not region specific	Automation and Decision Support Predictive Analytics	Merner, Erick
99	Improved freshwater runoff modelling in Eastern Canada to drive ocean models	Pilot	Use Neural Networks to post-process streamflow simulations from the WRF-Hydro model, improving their agreement with observational data. Thes...	Ecosystems and Oceans Science	National Capital Region	Automation and Decision Support Predictive Analytics	Winegardner, Amanda
113	Improving forecasting for high-priority ports	Pilot	Research into feasibility of improving accuracy and timeliness of short-term forecasts for coastal regions by combining traditional and AI m...	Ecosystems and Oceans Science	National Capital Region	Automation and Decision Support Predictive Analytics	Winegardner, Amanda
45	MyGCHR – Phoenix analysis tool	Ideation	Pro B data would be needed, but feed MyGCHR and Phoenix records, but flag those that are incorrect in the hopes of training the AI to recogn...	Human Resources and Corporate Services	National Capital Region	Automation and Decision Support Predictive Analytics	Gardner, Leigh
15	MyGCHR Phoenix analysis	Ideation	MYGCHR Phoenix analysis tool (Mark Jarvis) to flag incorrect records and recognize future incorrect entries to help cleanup pay files	Chief Digital Officer	National Capital Region	Automation and Decision Support Predictive Analytics	Gardner, Leigh
102	Past, Present and Future Oceanographic Conditions in Canada's MPAs and Marine OECMs (Other Effective area-based Conservation Measures)	Pilot	Downscaling climate models using neural networks for conservation areas. Neural networks allow us to represent coarse resolution climate pro...	Ecosystems and Oceans Science	National Capital Region	Cognitive and Generative AI Predictive Analytics	Winegardner, Amanda
31	Quality control of oceanographic data - Maritimes Region	Pilot	The goal of the project is to assist in the processing and quality control of CTD (oceanographic) data by leveraging machine learning models...	Strategic Policy	National Capital Region	Automation and Decision Support Predictive Analytics	Croft, Lee
69	Species distribution modeling	Pilot	Species distribution models for various coral/sponge/groundfish Random Forests	Ecosystems and Oceans Science	Pacific	Automation and Decision Support Predictive Analytics	Rooper, Chris
75	Study on the use of Artificial Neural Networks to Predict the Return Timing and Northern Diversion Rate for Migrating Fraser River Sockeye Salmon	Ideation	The Machine Learning feasibility study compliments the statistical approach by asking whether Artificial Intelligence (AI) methods can be de...	Ecosystems and Oceans Science	Pacific	Automation and Decision Support Predictive Analytics	Thomson, Richard

248
Analyse des tracés AIS en mer
Analyse des tracés AIS en utilisant l'IA: un IA a été créé pour analyser les tracés de bateau et déterminer si des activités de pêche ont lieu. Un pipeline pour les données AIS est en cours de création pour acheminer les données à l'AI. Les coordonnées des ZPM et zones de coraux éponges ont été fournies et si des activités dans ces zones sont détectées, l'info sera fournie à l'équipe de surveillance aérienne et maritime pour suivi

Pilot Conservation and Protection Automation and Decision Support Predictive Analytics
175
AI model for duplcate or dubious licence accounts
Licencing systems hold 10 times the amount accounts compared to licences issued. Many duplicate and dubious accounts across the millions of accounts. An AI model to discover duplicate/dubious accounts to highlight them for human review and action.

Ideation Programs Sector Automation and Decision Support Predictive Analytics
24
Automated Identification of Vessel Fishing Activity
Piloting 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 Predictive Analytics
120
Characterizing areas of convergence in the Saint John Harbour
This project is using a technique called self-organizing maps, which is an unsupervised machine learning algorithm, to identify patterns in areas of convergence near the Saint John Harbour. The preliminary analysis suggests that these patterns are related to different environmental conditions such as river discharge and tidal phase. The work has been conducted in collaboration with scientists from the Maritimes Region and is currently on hold.

Pilot Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
128
CICADA (cumulative effects spatial data tool) Phase 2: determining stressor effects and interactions to inform decision-making
Geospatial and machine learning techniques will be applied to determine the overall effect, stress(or) interactions, and relative influences of different stresses(ors) on fish metrics such as species occupancy, population status or community status.

Pilot Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
59
Clustering to characterize extreme marine conditions for the benthic region of the Northeastern Pacific continental margin
We introduce a method for characterizing extremes that uses machine learning to divide the data into regions with relatively consistent environmental conditions (temperature, oxygen, acidity), and define the extremes based on the historical statistics of variability of each of these fields. unsupervised clustering with k-means

Pilot Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
65
Comprehensive marine substrate classification applied to Canada’s Pacific shelf
We built five regional and one coastwide substrate model for the BC coast using substrate observations and seafloor bathymetry derivatives and oceanographic predictors using Random Forests. Random Forests

Pilot Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
101
Creating space-time continuous surface chlorophyll fields for northwest Atlantic using neural network methods
Neural network methods are applied to fill the space-time gaps in satellite remote sensing data of surface chlorophyll for the Northwest Atlantic. The generated dataset will be validated and analyzed.

Pilot Ecosystems and Oceans Science Cognitive and Generative AI Predictive Analytics
32
CTD Anomaly Detection
The goal of this project is to apply machine learning to detect anomalous oceanographic data, highlighting meaningful physical phenomena in the state of the ocean for closer investigation by scientists. A proof of conept model has been developed to demonstrate feasibility. Further validation is required with the client to verify alignment with the needs of scientists. Client: Pacific Region - Ocean Sciences Division

Pilot Strategic Policy Perception and Understanding Predictive Analytics
61
CTD data QC using ML
Working with CDOS office to streamline our CTD QC using ML cnn and others

Pilot Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
41
Cyber Security Operation
Cyber Security Operation Centre has deployed AI/ML in MS Defender (Endpoint Detection and Response (EDR) system), MS Sentinel (Security Incident and Event Management (SIEM) system), Data Lost Prevention (DLP), and Exchange Online Protection (EOP) for real-time threat detection.

Production Chief Digital Officer Automation and Decision Support Predictive Analytics
208
Fishing Detection
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.

Pilot Strategic Policy Automation and Decision Support Predictive Analytics
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

Ideation Chief Digital Officer Automation and Decision Support Predictive Analytics
99
Improved freshwater runoff modelling in Eastern Canada to drive ocean models
Use 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.

Pilot Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
113
Improving forecasting for high-priority ports
Research into feasibility of improving accuracy and timeliness of short-term forecasts for coastal regions by combining traditional and AI modelling methods.

Pilot Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
45
MyGCHR – Phoenix analysis tool
Pro B data would be needed, but feed MyGCHR and Phoenix records, but flag those that are incorrect in the hopes of training the AI to recognize future incorrect entries and to help clean up pay files for employees. P&C is in negotiation currently with PSPC to ‘own’ our backlog of Phoenix cases, so this could be a big help.

Ideation Human Resources and Corporate Services Automation and Decision Support Predictive Analytics
15
MyGCHR Phoenix analysis
MYGCHR Phoenix analysis tool (Mark Jarvis) to flag incorrect records and recognize future incorrect entries to help cleanup pay files

Ideation Chief Digital Officer Automation and Decision Support Predictive Analytics
102
Past, Present and Future Oceanographic Conditions in Canada's MPAs and Marine OECMs (Other Effective area-based Conservation Measures)
Downscaling climate models using neural networks for conservation areas. Neural networks allow us to represent coarse resolution climate projections at a much higher spatial resolution across six eastern MPAs.

Pilot Ecosystems and Oceans Science Cognitive and Generative AI Predictive Analytics
31
Quality control of oceanographic data - Maritimes Region
The goal of the project is to assist in the processing and quality control of CTD (oceanographic) data by leveraging machine learning models to predict quality flags for the CTD data products generated by Maritimes region, enabling faster data processing by reducing manual burden. Initial explorations have been conducted to investigate transferability of existing quality control models from Pacific region. Based on investigation results, new models will be trained specific to Maritimes region. Client: Maritimes Region - Ocean Data

Pilot Strategic Policy Automation and Decision Support Predictive Analytics
69
Species distribution modeling
Species distribution models for various coral/sponge/groundfish Random Forests

Pilot Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics
75
Study on the use of Artificial Neural Networks to Predict the Return Timing and Northern Diversion Rate for Migrating Fraser River Sockeye Salmon
The Machine Learning feasibility study compliments the statistical approach by asking whether Artificial Intelligence (AI) methods can be developed to predict salmon behaviour as functions of ocean conditions. classical machine learning models, including linear regression, Ridge regression, and Random Forest

Ideation Ecosystems and Oceans Science Automation and Decision Support Predictive Analytics