Registre des initiatives en IA

21 initiative(s) courante(s)

• 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 

  Pilote Conservation et protection Automatisation et aide à la décision Analytique prédictive

• 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 Analytique prédictive

• 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 Analytique prédictive

• 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.

  Idéation Secteur des programmes Automatisation et aide à la décision Analytique prédictive

• 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.

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

• 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.

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

• 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.

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

• 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.

  Pilote Sciences des écosystèmes et des océans IA cognitive et générative Analytique prédictive

• 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.   

  Pilote Sciences des écosystèmes et des océans IA cognitive et générative Analytique prédictive

• 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.

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

• 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

  Idéation Sciences des écosystèmes et des océans Automatisation et aide à la décision Analytique prédictive

• 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 Analytique prédictive

• 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

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

• 61

  CTD data QC using ML

  Working with CDOS office to streamline our CTD QC using ML cnn and others

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

• 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

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

• 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.

  Idéation Ressources humaines et services de gestion Automatisation et aide à la décision Analytique prédictive

• 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 Dirigeant principal du numérique Automatisation et aide à la décision Analytique prédictive

• 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

  Pilote Politiques stratégiques Perception et compréhension Analytique prédictive

• 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

  Pilote Politiques stratégiques Automatisation et aide à la décision Analytique prédictive

• 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.

  Pilote Secteur des programmes Automatisation et aide à la décision Analytique prédictive

• 15

  MyGCHR Phoenix analysis

  MYGCHR Phoenix analysis tool (Mark Jarvis) to flag incorrect records and recognize future incorrect entries to help cleanup pay files

  Idéation Dirigeant principal du numérique Automatisation et aide à la décision Analytique prédictive