Classification of benthic substrates using supervised and unsupervised machine learning models on the North Shore of the St. Lawrence maritime estuary

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The aim of this project is to generate benthic substrate classification datasets using a supervised machine learning model (trained with field truth data) and an unsupervised machine learning model (trained without field truth data). These two models use Hackel parameters calculated with bathymetry data. Hackel parameters are the parameters characterizing the geometry of the seabed, such as flatness, linearity, sphericity, verticality, and surface variation.

Bathymetric data were acquired with a Seabat 7125 multibeam sounder (1x1m resolution) from October 15, 2018 to October 18, 2019 on the north shore of the Maritime Estuary (from St-Ludger Bay to Godbout) and post-processed with the CUBE method.

Concerning substrate classification data, two datasets were generated with two machine learning models:

A first model trained with field truth data from Fisheries and Oceans Canada and using a gradient reinforcement method.
A second model trained without field truth data and based on a Gaussian mixture method.

The generation of the final classification datasets was finalized on May 16, 2023.

The aim of generating this data is to facilitate the classification of substrates for various fields (fishing, dredging, gas and oil) via artificial intelligence, and to make it more accessible because it is less expensive from an operational point of view.

This project was funded by Fisheries and Oceans Canada's Coastal Environmental Baseline Program under the Oceans Protection Plan.

For further information, please consult the following paper: Labbé-Morissette, G., Leclercq, T., Charron-Morneau, P., Gonthier, D., Doiron, D., Chouaer, M. A., & Munang, D. N. (2024). Classification of Coastal Benthic Substrates Using Supervised and Unsupervised Machine Learning Models on North Shore of the St. Lawrence Maritime Estuary (Canada). Geomatics, 4(3), 237-252. https://doi.org/10.3390/geomatics4030013, 2024.

Data and resources

Baie des Anglais - Bathymetry and Substrates Classification Data

Form for creating a graph with Baie des Anglais gridded data

ERDDAP Open
Franquelin - Bathymetry and Substrates Classification Data

Form for creating a graph with Franquelin gridded data

ERDDAP Open
Godbout - Bathymetry and Substrates Classification Data

Form for creating a graph with Godbout gridded data

ERDDAP Open
Grosse Pointe - Bathymetry and Substrates Classification Data

Form for creating a graph with Grosse Pointe gridded data

ERDDAP Open
Pointe Paradis - Bathymetry and Substrates Classification Data

Form for creating a graph with Pointe Paradis gridded data

ERDDAP Open
Marina de Baie-Comeau - Bathymetry and Substrates Classification Data

Form for creating a graph with Marina Baie-Comeau gridded data

ERDDAP Open
Pointe-Lebel - Bathymetry and Substrates Classification Data

Form for creating a graph with Pointe-Lebel gridded data

ERDDAP Open
St. Ludger - Bathymetry and Substrates Classification Data

Form for creating a graph with St. Ludger gridded data

ERDDAP Open
Images of the Bathymetry and Substrates Classification Models

Georeferenced images of the project (GeoTIFF)

HTML Open
Bathymetry and substrates classification models non-gridded data

Non-gridded and normalised data in CSV format.

HTML Open

Additional information

DOI	https://doi.org/10.26071/ogsl-66c132fc-b150
Temporal extent	2018-10-15 – 2019-10-18
Vertical extent	-1.17 – 72.048 m
Date (creation)	2018-10-15
Date (revision)	2024-09-12
Status	completed
Update frequency	asNeeded
Metadata language	fr
Projects	Coastal Environmental Baseline Program
License	Creative Commons Attribution 4.0
Source metadata	ISO 19115 XML
Last updated	2026-07-31
Created	2026-07-31
API	package_show