Reference number : GC-2017-Q1-00061, amendment number : , agreement type : G, recipient legal name : Campbell, Anne (Nova Scotia Community College), recipient country : CA, recipient province : nan, recipient city : Nova Scotia|Nouvelle-Écosse, recipient postal code : nan, program name english : Engage Grants for Colleges, program name french : Subventions d'engagement partenarial pour les collèges, program purpose english : Seabed classification involves the analysis of acoustic remote sensing data to characterize the physical,_x000D_
geological, and biological properties of ocean, lake, and river beds for production of thematic maps. Acoustic_x000D_
remote sensing can be done using almost any sonar, but multibeam echo sounders (MBES) have become a_x000D_
favored and widely used technique in recent years for seafloor reconnaissance surveys in a wide range of ocean_x000D_
sector industries. MBES record information relating to seafloor bathymetry (depth), and acoustic seafloor_x000D_
backscatter strength (which can be used to infer seafloor geological and habitat characteristics). Automated_x000D_
software methods offer an objective and robust classification approach compared to conventional expert "by_x000D_
eye" interpretation of such data sets to produce interpreted maps of seafloor features (e.g. maps of seafloor_x000D_
sediments or seafloor habitats). Even though software classification methods have progressed over the past_x000D_
decade, there is still significant scope for innovation and improvement of software tools for semi-automated_x000D_
classification of the MBES data sets. In this collaborative project, NSCC will partner with Maritime Way_x000D_
Scientific Ltd. to explore new and innovative segmentation routines for improved seafloor classification and_x000D_
characterization of MBES data sets. These routines will be tested on MBES data sets held by NSCC to_x000D_
validation performance compared against in situ ground-truthing of the seafloor geological and habitat_x000D_
characteristics. The anticipated scientific and technical advances offer direct value to the private sector partner_x000D_
by providing classification/segmentation routines for improvement of their seafloor classification software_x000D_
products. This will meet market demand for a wide cross section of their clients using the software for MBES_x000D_
backscatter classification, with follow-on benefits for improved thematic map production across a wide cross_x000D_
section of ocean sector industries (i.e. benefits afforded by improved seafloor map production for application_x000D_
for oil and gas exploration, seafloor cable routing, fisheries and conservation mapping)., program purpose french : Seabed classification involves the analysis of acoustic remote sensing data to characterize the physical,_x000D_
geological, and biological properties of ocean, lake, and river beds for production of thematic maps. Acoustic_x000D_
remote sensing can be done using almost any sonar, but multibeam echo sounders (MBES) have become a_x000D_
favored and widely used technique in recent years for seafloor reconnaissance surveys in a wide range of ocean_x000D_
sector industries. MBES record information relating to seafloor bathymetry (depth), and acoustic seafloor_x000D_
backscatter strength (which can be used to infer seafloor geological and habitat characteristics). Automated_x000D_
software methods offer an objective and robust classification approach compared to conventional expert "by_x000D_
eye" interpretation of such data sets to produce interpreted maps of seafloor features (e.g. maps of seafloor_x000D_
sediments or seafloor habitats). Even though software classification methods have progressed over the past_x000D_
decade, there is still significant scope for innovation and improvement of software tools for semi-automated_x000D_
classification of the MBES data sets. In this collaborative project, NSCC will partner with Maritime Way_x000D_
Scientific Ltd. to explore new and innovative segmentation routines for improved seafloor classification and_x000D_
characterization of MBES data sets. These routines will be tested on MBES data sets held by NSCC to_x000D_
validation performance compared against in situ ground-truthing of the seafloor geological and habitat_x000D_
characteristics. The anticipated scientific and technical advances offer direct value to the private sector partner_x000D_
by providing classification/segmentation routines for improvement of their seafloor classification software_x000D_
products. This will meet market demand for a wide cross section of their clients using the software for MBES_x000D_
backscatter classification, with follow-on benefits for improved thematic map production across a wide cross_x000D_
section of ocean sector industries (i.e. benefits afforded by improved seafloor map production for application_x000D_
for oil and gas exploration, seafloor cable routing, fisheries and conservation mapping)., agreement title english : Developing methods for automated multibeam backscatter classification, agreement title french : Developing methods for automated multibeam backscatter classification, agreement number : CARD1, agreement value : 25000.0, agreement start date : 2017-06-14, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada