cidcoBenthicSubstrateAiGrossePointe eng UTF8 dataset service CIOOS Data Manager Canadian Integrated Ocean Observing System 19999999999 6375 Edzell Castle Circle, 2nd Floor, Steele Ocean Sciences Building Halifax NS B3H 4J1 CA info@cioos.ca pointOfContact 2026-08-17 ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for Imagery and Gridded Data ISO 19115-2:2009(E) 3 column 772 1.402075226978784E-5 row 447 9.035874439462331E-6 temporal 1 area Classification of Benthic Substrates by Artificial Intelligence on the St. Lawrence Shoreline - Grosse Pointe | Classification des substrats benthiques par intelligence artificielle sur le littoral du Saint-Laurent - Grosse Pointe 2026-08-17 creation erddap.cioos.ca cidcoBenthicSubstrateAiGrossePointe Interdisciplinary Centre for the Development of Ocean Mapping https://www.cidco.ca/ information web browser Background Information Background information from the source information originator Guillaume Labbé-Morissette, Patrick Charron-Morneau, Yanick Gendreau, Théau Leclercq, Dominic Ndeh Munang contributor The substrate classification data were generated with two machine learning models: (1) A first model trained with field truth data from Fisheries and Oceans Canada and using a gradient reinforcement method. (2) A second model trained without field truth data and based on a Gaussian mixture method. 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. Interdisciplinary Centre for the Development of Ocean Mapping https://www.cidco.ca/ information web browser Background Information Background information from the source information pointOfContact artificial intelligence benthic substrates classification benthic substrates mapping supervised machine learning unsupervised machine learning theme Interdisciplinary Centre for the Development of Ocean Mapping project time latitude longitude sea_floor_depth_below_sea_surface theme CF Standard Name Table v79 Creative Commons Attribution 4.0 International license CC-BY 4.0. Allows for open sharing and adaptation of the data provided that the original creator is attributed Interdisciplinary Centre for the Development of Ocean Mapping largerWorkCitation project Unidata Common Data Model Grid largerWorkCitation project eng geoscientificInformation 1 -68.38731 -68.3765 49.02966 49.03369 seconds 2018-10-19 2018-10-19 Classification of Benthic Substrates by Artificial Intelligence on the St. Lawrence Shoreline - Grosse Pointe | Classification des substrats benthiques par intelligence artificielle sur le littoral du Saint-Laurent - Grosse Pointe 2026-08-17 creation Interdisciplinary Centre for the Development of Ocean Mapping https://www.cidco.ca/ information web browser Background Information Background information from the source information originator Guillaume Labbé-Morissette, Patrick Charron-Morneau, Yanick Gendreau, Théau Leclercq, Dominic Ndeh Munang contributor The substrate classification data were generated with two machine learning models: (1) A first model trained with field truth data from Fisheries and Oceans Canada and using a gradient reinforcement method. (2) A second model trained without field truth data and based on a Gaussian mixture method. 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. ERDDAP griddap 1 -68.38731 -68.3765 49.02966 49.03369 seconds 2018-10-19 2018-10-19 tight ERDDAPgriddapDatasetQueryAndAccess https://erddap.cioos.ca/erddap/griddap/cidcoBenthicSubstrateAiGrossePointe ERDDAP:griddap ERDDAP-griddap ERDDAP's griddap service (a flavor of OPeNDAP) for gridded data. Add different extensions (e.g., .html, .graph, .das, .dds) to the base URL for different purposes. download Classification of Benthic Substrates by Artificial Intelligence on the St. Lawrence Shoreline - Grosse Pointe | Classification des substrats benthiques par intelligence artificielle sur le littoral du Saint-Laurent - Grosse Pointe 2026-08-17 creation Interdisciplinary Centre for the Development of Ocean Mapping https://www.cidco.ca/ information web browser Background Information Background information from the source information originator Guillaume Labbé-Morissette, Patrick Charron-Morneau, Yanick Gendreau, Théau Leclercq, Dominic Ndeh Munang contributor The substrate classification data were generated with two machine learning models: (1) A first model trained with field truth data from Fisheries and Oceans Canada and using a gradient reinforcement method. (2) A second model trained without field truth data and based on a Gaussian mixture method. 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. OPeNDAP 1 -68.38731 -68.3765 49.02966 49.03369 seconds 2018-10-19 2018-10-19 tight OPeNDAPDatasetQueryAndAccess https://erddap.cioos.ca/erddap/griddap/cidcoBenthicSubstrateAiGrossePointe OPeNDAP:OPeNDAP OPeNDAP An OPeNDAP service for gridded data. Add different extensions (e.g., .html, .das, .dds) to the base URL for different purposes. download Classification of Benthic Substrates by Artificial Intelligence on the St. Lawrence Shoreline - Grosse Pointe | Classification des substrats benthiques par intelligence artificielle sur le littoral du Saint-Laurent - Grosse Pointe 2026-08-17 creation Interdisciplinary Centre for the Development of Ocean Mapping https://www.cidco.ca/ information web browser Background Information Background information from the source information originator Guillaume Labbé-Morissette, Patrick Charron-Morneau, Yanick Gendreau, Théau Leclercq, Dominic Ndeh Munang contributor The substrate classification data were generated with two machine learning models: (1) A first model trained with field truth data from Fisheries and Oceans Canada and using a gradient reinforcement method. (2) A second model trained without field truth data and based on a Gaussian mixture method. 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. Open Geospatial Consortium Web Map Service (WMS) 1 -68.38731 -68.3765 49.02966 49.03369 seconds 2018-10-19 2018-10-19 tight GetCapabilities https://erddap.cioos.ca/erddap/wms/cidcoBenthicSubstrateAiGrossePointe/request?service=WMS&version=1.3.0&request=GetCapabilities OGC:WMS OGC-WMS Open Geospatial Consortium Web Map Service (WMS) download physicalMeasurement sea_floor_depth_below_sea_surface float Sea Floor Depth boosting_class_id float Boosting Class ID gmm_class_id float GMM Class ID CIOOS Data Manager Canadian Integrated Ocean Observing System 19999999999 6375 Edzell Castle Circle, 2nd Floor, Steele Ocean Sciences Building Halifax NS B3H 4J1 CA info@cioos.ca distributor OPeNDAP DAP/2.0 https://erddap.cioos.ca/erddap/griddap/cidcoBenthicSubstrateAiGrossePointe.html order Data Subset Form ERDDAP's version of the OPeNDAP .html web page for this dataset. Specify a subset of the dataset and download the data via OPeNDAP or in many different file types. download https://erddap.cioos.ca/erddap/griddap/cidcoBenthicSubstrateAiGrossePointe.graph order Make-A-Graph Form ERDDAP's Make-A-Graph .html web page for this dataset. Create an image with a map or graph of a subset of the data. mapDigital dataset Data and metadata were standardized and then checked with the SLGO data validation tool. The data were converted from the NAD83 / UTM zone 19N coordinate system to the WGS84 coordinate system and transformed from a tabular to a gridded format This record was created from dataset metadata by ERDDAP Version 2.28.1