Registre des initiatives en IA

197 initiative(s) courante(s)

• 86

  Automatic detection of Arctic whales in aerial images

  In 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.

  Pilote Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 87

  ANMPA Habitat Mapping Project

  Drop 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 Vision par ordinateur

• 88

  Development of an AI matching model for bowhead whale photo-identification

  Using 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 Vision par ordinateur

• 89

  Augmenting Whale Detection in Satellite Images using synthetic data

  An 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 Vision par ordinateur

• 90

  Deep Learning Enhances Beluga Whale Research in the Arctic

  Esri 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 Vision par ordinateur

• 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 Vision par ordinateur

• 84

  Daily Use

  Dashboard Development, Used the standalone M365 version of Copilot to assist with coding.

  Pilote Secteur des programmes IA cognitive et générative IA générative (texte, images, code)

• 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 Vision par ordinateur

• 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 Vision par ordinateur

• 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 Vision par ordinateur

• 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 Analytique prescriptive

• 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

• 100

  Development of AI technology for otolith and scale ageing

  Ageing 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 Vision par ordinateur

• 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

• 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

• 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 Vision par ordinateur

• 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 Vision par ordinateur

• 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

• 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 Vision par ordinateur

• 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

• 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

• 62

  Deep sea habitat, organism and object detection and identification.

  Proposing a capstone project to Computer Science students from Camosun College to develop a pipeline for identifying objects and loading the results into Biigle for human review. TBD.

  Idéation Sciences des écosystèmes et des océans Perception et compréhension Vision par ordinateur

• 63

  Factoid Finder Tool

  The Factoid Finder extracts text from machine-readable PDFs to create searchable content libraries. Small Language Models are used to implement semantic search techniques, allowing users to search the library for relevant passages within the PDFs. Small Language Model and Text Embedder: MS Marco Distilbert Dot-v5 Cross-encoder: MS Marco MiniLM-L6-v2

  Pilote IA cognitive et générative Gestion des connaissances

• 64

  Automatic detection of unidentified fish sounds

  We 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 Détection d'événements sonores

• 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