�ݺ�ߣshows by User: giumas

�ݺ�ߣshows by User: giumas / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: giumas / Sat, 12 Apr 2025 13:14:19 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: giumas US Hydro 2025 - New Hydrographic Survey Specifications: Updates and Enhancements /slideshow/us-hydro-2025-new-hydrographic-survey-specifications-updates-and-enhancements/277855217 ushydro2025hssdmattwilson-250412131419-77ada7eb
New Hydrographic Survey Specifications: Updates and Enhancements US Hydro Conference March 20, 2025 Matt Wilson, Tyanne Faulkes, Giuseppe Masetti The Hydrographic Surveys Specifications and Deliverables (HSSD) document, published in April 2024 by NOAA OCS, represented the largest overhaul of the HSSD in more than 20 years. The new specifications were delivered with updated tools, jointly developed with CCOM/JHC and NOAA HSTB. This presentation will discuss the adjustments made to the HSSD and its accompanying toolset during the first year of use. Ensuring accurate IHO S-100 based metadata entry, on both a survey specific and grid specific basis, is most imperative, and as a result, a metadata attribution guide was added as an appendix to the HSSD. Additional reporting considerations were in-focus, as OCS seeks to strike the best balance between those who prefer the traditional narrative approach, versus a machine-readable XML metadata file. New quality control methods for Bathymetric Attributed Grid (BAG) files to ensure their rapid throughput were prompted from the National Bathymetric Source (NBS), and feedback from the processing branches in Norfolk and Seattle led to the development of new components of HydrOffice QC Tools 4, which automates the application of the HSSD to ensure the completeness and accuracy of the field submission. Lastly, this presentation will discuss continuing topics related to potential changes to the HSSD that are ongoing in 2025. ]]>
New Hydrographic Survey Specifications: Updates and Enhancements US Hydro Conference March 20, 2025 Matt Wilson, Tyanne Faulkes, Giuseppe Masetti The Hydrographic Surveys Specifications and Deliverables (HSSD) document, published in April 2024 by NOAA OCS, represented the largest overhaul of the HSSD in more than 20 years. The new specifications were delivered with updated tools, jointly developed with CCOM/JHC and NOAA HSTB. This presentation will discuss the adjustments made to the HSSD and its accompanying toolset during the first year of use. Ensuring accurate IHO S-100 based metadata entry, on both a survey specific and grid specific basis, is most imperative, and as a result, a metadata attribution guide was added as an appendix to the HSSD. Additional reporting considerations were in-focus, as OCS seeks to strike the best balance between those who prefer the traditional narrative approach, versus a machine-readable XML metadata file. New quality control methods for Bathymetric Attributed Grid (BAG) files to ensure their rapid throughput were prompted from the National Bathymetric Source (NBS), and feedback from the processing branches in Norfolk and Seattle led to the development of new components of HydrOffice QC Tools 4, which automates the application of the HSSD to ensure the completeness and accuracy of the field submission. Lastly, this presentation will discuss continuing topics related to potential changes to the HSSD that are ongoing in 2025. ]]> Sat, 12 Apr 2025 13:14:19 GMT /slideshow/us-hydro-2025-new-hydrographic-survey-specifications-updates-and-enhancements/277855217 giumas@slideshare.net(giumas) US Hydro 2025 - New Hydrographic Survey Specifications: Updates and Enhancements giumas New Hydrographic Survey Specifications: Updates and Enhancements US Hydro Conference March 20, 2025 Matt Wilson, Tyanne Faulkes, Giuseppe Masetti The Hydrographic Surveys Specifications and Deliverables (HSSD) document, published in April 2024 by NOAA OCS, represented the largest overhaul of the HSSD in more than 20 years. The new specifications were delivered with updated tools, jointly developed with CCOM/JHC and NOAA HSTB. This presentation will discuss the adjustments made to the HSSD and its accompanying toolset during the first year of use. Ensuring accurate IHO S-100 based metadata entry, on both a survey specific and grid specific basis, is most imperative, and as a result, a metadata attribution guide was added as an appendix to the HSSD. Additional reporting considerations were in-focus, as OCS seeks to strike the best balance between those who prefer the traditional narrative approach, versus a machine-readable XML metadata file. New quality control methods for Bathymetric Attributed Grid (BAG) files to ensure their rapid throughput were prompted from the National Bathymetric Source (NBS), and feedback from the processing branches in Norfolk and Seattle led to the development of new components of HydrOffice QC Tools 4, which automates the application of the HSSD to ensure the completeness and accuracy of the field submission. Lastly, this presentation will discuss continuing topics related to potential changes to the HSSD that are ongoing in 2025. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ushydro2025hssdmattwilson-250412131419-77ada7eb-thumbnail.jpg?width=120&height=120&fit=bounds" /> New Hydrographic Survey Specifications: Updates and Enhancements US Hydro Conference March 20, 2025 Matt Wilson, Tyanne Faulkes, Giuseppe Masetti The Hydrographic Surveys Specifications and Deliverables (HSSD) document, published in April 2024 by NOAA OCS, represented the largest overhaul of the HSSD in more than 20 years. The new specifications were delivered with updated tools, jointly developed with CCOM/JHC and NOAA HSTB. This presentation will discuss the adjustments made to the HSSD and its accompanying toolset during the first year of use. Ensuring accurate IHO S-100 based metadata entry, on both a survey specific and grid specific basis, is most imperative, and as a result, a metadata attribution guide was added as an appendix to the HSSD. Additional reporting considerations were in-focus, as OCS seeks to strike the best balance between those who prefer the traditional narrative approach, versus a machine-readable XML metadata file. New quality control methods for Bathymetric Attributed Grid (BAG) files to ensure their rapid throughput were prompted from the National Bathymetric Source (NBS), and feedback from the processing branches in Norfolk and Seattle led to the development of new components of HydrOffice QC Tools 4, which automates the application of the HSSD to ensure the completeness and accuracy of the field submission. Lastly, this presentation will discuss continuing topics related to potential changes to the HSSD that are ongoing in 2025.

US Hydro 2025 - New Hydrographic Survey Specifications: Updates and Enhancements from Giuseppe Masetti

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0 Open Backscatter Toolchain (OpenBST) Project - A Community-vetted Workflow for Backscatter Processing /slideshow/open-backscatter-toolchain-openbst-project-a-communityvetted-workflow-for-backscatter-processing/229396305 msmithcanadianhydro2020v1-200228150235
Presentation given at the Canadian Hydrographic Conference 2020 Dates: Mon., Feb. 24, 2020 – Thu., Feb. 27, 2020 Location: Quebec City, Canada Authors: M. Smith, G. Masetti, L. Mayer, M. Malik, J.-M. Augustin, C. Poncelet, I. Parnum ]]>
Presentation given at the Canadian Hydrographic Conference 2020 Dates: Mon., Feb. 24, 2020 – Thu., Feb. 27, 2020 Location: Quebec City, Canada Authors: M. Smith, G. Masetti, L. Mayer, M. Malik, J.-M. Augustin, C. Poncelet, I. Parnum ]]> Fri, 28 Feb 2020 15:02:35 GMT /slideshow/open-backscatter-toolchain-openbst-project-a-communityvetted-workflow-for-backscatter-processing/229396305 giumas@slideshare.net(giumas) Open Backscatter Toolchain (OpenBST) Project - A Community-vetted Workflow for Backscatter Processing giumas Presentation given at the Canadian Hydrographic Conference 2020 Dates: Mon., Feb. 24, 2020 – Thu., Feb. 27, 2020 Location: Quebec City, Canada Authors: M. Smith, G. Masetti, L. Mayer, M. Malik, J.-M. Augustin, C. Poncelet, I. Parnum <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/msmithcanadianhydro2020v1-200228150235-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation given at the Canadian Hydrographic Conference 2020 Dates: Mon., Feb. 24, 2020 – Thu., Feb. 27, 2020 Location: Quebec City, Canada Authors: M. Smith, G. Masetti, L. Mayer, M. Malik, J.-M. Augustin, C. Poncelet, I. Parnum

Open Backscatter Toolchain (OpenBST) Project - A Community-vetted Workflow for Backscatter Processing from Giuseppe Masetti

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0 e-learning Python for Ocean Mapping - Empowering the next generation of ocean mappers with effective programming skills /slideshow/elearning-python-for-ocean-mapping-empowering-the-next-generation-of-ocean-mappers-with-effective-programming-skills/229332713 chc2020epommasettietal-200227183722
Presentation given at the Canadian Hydrographic Conference 2020 Dates: Mon., Feb. 24, 2020 – Thu., Feb. 27, 2020 Location: Quebec City, Canada Authors: G. Masetti, S. Dijkstra, R. Wigley, S. Greenaway, D. Manda, A. Armstrong, and L. Mayer]]>
Presentation given at the Canadian Hydrographic Conference 2020 Dates: Mon., Feb. 24, 2020 – Thu., Feb. 27, 2020 Location: Quebec City, Canada Authors: G. Masetti, S. Dijkstra, R. Wigley, S. Greenaway, D. Manda, A. Armstrong, and L. Mayer]]> Thu, 27 Feb 2020 18:37:22 GMT /slideshow/elearning-python-for-ocean-mapping-empowering-the-next-generation-of-ocean-mappers-with-effective-programming-skills/229332713 giumas@slideshare.net(giumas) e-learning Python for Ocean Mapping - Empowering the next generation of ocean mappers with effective programming skills giumas Presentation given at the Canadian Hydrographic Conference 2020 Dates: Mon., Feb. 24, 2020 – Thu., Feb. 27, 2020 Location: Quebec City, Canada Authors: G. Masetti, S. Dijkstra, R. Wigley, S. Greenaway, D. Manda, A. Armstrong, and L. Mayer <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/chc2020epommasettietal-200227183722-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation given at the Canadian Hydrographic Conference 2020 Dates: Mon., Feb. 24, 2020 – Thu., Feb. 27, 2020 Location: Quebec City, Canada Authors: G. Masetti, S. Dijkstra, R. Wigley, S. Greenaway, D. Manda, A. Armstrong, and L. Mayer

e-learning Python for Ocean Mapping - Empowering the next generation of ocean mappers with effective programming skills from Giuseppe Masetti

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0 Leveraging Predictions from NOAA’s Oceanographic Forecast Models to Increase Environmental Variability Awareness in Ocean Mapping /slideshow/leveraging-predictions-from-noaas-oceanographic-forecast-models-to-increase-environmental-variability-awareness-in-ocean-mapping/221668901 ams100j68-200119205711
Presentation given at the 100th AMS Annual Meeting in Boston, MA. January 16, 2020.]]>
Presentation given at the 100th AMS Annual Meeting in Boston, MA. January 16, 2020.]]> Sun, 19 Jan 2020 20:57:11 GMT /slideshow/leveraging-predictions-from-noaas-oceanographic-forecast-models-to-increase-environmental-variability-awareness-in-ocean-mapping/221668901 giumas@slideshare.net(giumas) Leveraging Predictions from NOAA’s Oceanographic Forecast Models to Increase Environmental Variability Awareness in Ocean Mapping giumas Presentation given at the 100th AMS Annual Meeting in Boston, MA. January 16, 2020. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ams100j68-200119205711-thumbnail.jpg?width=120&height=120&fit=bounds" /> Presentation given at the 100th AMS Annual Meeting in Boston, MA. January 16, 2020.

Leveraging Predictions from NOAA’s Oceanographic Forecast Models to Increase Environmental Variability Awareness in Ocean Mapping from Giuseppe Masetti

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0 ePOM - Fundamentals of Research Software Development - Code Version Control /slideshow/epom-fundamentals-of-research-software-development-code-version-control/196118853 epom-191121185934
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the "Code Version Control" module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom ]]>
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the "Code Version Control" module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom ]]> Thu, 21 Nov 2019 18:59:34 GMT /slideshow/epom-fundamentals-of-research-software-development-code-version-control/196118853 giumas@slideshare.net(giumas) ePOM - Fundamentals of Research Software Development - Code Version Control giumas E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the "Code Version Control" module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/epom-191121185934-thumbnail.jpg?width=120&height=120&fit=bounds" /> E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the "Code Version Control" module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom

ePOM - Fundamentals of Research Software Development - Code Version Control from Giuseppe Masetti

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0 ePOM - Fundamentals of Research Software Development - Integrated Development Environment /slideshow/epom-fundamentals-of-research-software-development-integrated-development-environment/194452798 epom-191117153347
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the "Integrated Development Environment" module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom ]]>
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the "Integrated Development Environment" module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom ]]> Sun, 17 Nov 2019 15:33:47 GMT /slideshow/epom-fundamentals-of-research-software-development-integrated-development-environment/194452798 giumas@slideshare.net(giumas) ePOM - Fundamentals of Research Software Development - Integrated Development Environment giumas E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the "Integrated Development Environment" module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/epom-191117153347-thumbnail.jpg?width=120&height=120&fit=bounds" /> E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the "Integrated Development Environment" module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom

ePOM - Fundamentals of Research Software Development - Integrated Development Environment from Giuseppe Masetti

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0 ePOM - Fundamentals of Research Software Development - Introduction /giumas/epom-fundamentals-of-research-software-development-introduction epom-191115132444
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Introduction module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom ]]>
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Introduction module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom ]]> Fri, 15 Nov 2019 13:24:44 GMT /giumas/epom-fundamentals-of-research-software-development-introduction giumas@slideshare.net(giumas) ePOM - Fundamentals of Research Software Development - Introduction giumas E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Introduction module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/epom-191115132444-thumbnail.jpg?width=120&height=120&fit=bounds" /> E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Introduction module (part of the Fundamentals of Research Software Development training). More details at https://www.hydroffice.org/epom

ePOM - Fundamentals of Research Software Development - Introduction from Giuseppe Masetti

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0 ePOM - Intro to Ocean Data Science - Raster and Vector Data Formats /slideshow/epom-intro-to-ocean-data-science-raster-and-vector-data-formats/170662461 epom-190910181513
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Raster and Vector Data Formats module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom ]]>
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Raster and Vector Data Formats module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom ]]> Tue, 10 Sep 2019 18:15:13 GMT /slideshow/epom-intro-to-ocean-data-science-raster-and-vector-data-formats/170662461 giumas@slideshare.net(giumas) ePOM - Intro to Ocean Data Science - Raster and Vector Data Formats giumas E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Raster and Vector Data Formats module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/epom-190910181513-thumbnail.jpg?width=120&height=120&fit=bounds" /> E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Raster and Vector Data Formats module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom

ePOM - Intro to Ocean Data Science - Raster and Vector Data Formats from Giuseppe Masetti

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0 ePOM - Intro to Ocean Data Science - Scientific Computing /slideshow/epom-intro-to-ocean-data-science-scientific-computing/169343958 epom-190905172435
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Scientific Computing module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom ]]>
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Scientific Computing module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom ]]> Thu, 05 Sep 2019 17:24:35 GMT /slideshow/epom-intro-to-ocean-data-science-scientific-computing/169343958 giumas@slideshare.net(giumas) ePOM - Intro to Ocean Data Science - Scientific Computing giumas E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Scientific Computing module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/epom-190905172435-thumbnail.jpg?width=120&height=120&fit=bounds" /> E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Scientific Computing module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom

ePOM - Intro to Ocean Data Science - Scientific Computing from Giuseppe Masetti

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0 ePOM - Intro to Ocean Data Science - Data Visualization /slideshow/epom-intro-to-ocean-data-science-data-visualization/168771240 epom-190903173635
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Data Visualization module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom ]]>
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Data Visualization module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom ]]> Tue, 03 Sep 2019 17:36:35 GMT /slideshow/epom-intro-to-ocean-data-science-data-visualization/168771240 giumas@slideshare.net(giumas) ePOM - Intro to Ocean Data Science - Data Visualization giumas E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Data Visualization module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/epom-190903173635-thumbnail.jpg?width=120&height=120&fit=bounds" /> E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Data Visualization module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom

ePOM - Intro to Ocean Data Science - Data Visualization from Giuseppe Masetti

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0 ePOM - Intro to Ocean Data Science - Object-Oriented Programming /slideshow/epom-intro-to-ocean-data-science-objectoriented-programming-167527824/167527824 epom-190829181822
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Object-Oriented Programming module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom ]]>
E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Object-Oriented Programming module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom ]]> Thu, 29 Aug 2019 18:18:22 GMT /slideshow/epom-intro-to-ocean-data-science-objectoriented-programming-167527824/167527824 giumas@slideshare.net(giumas) ePOM - Intro to Ocean Data Science - Object-Oriented Programming giumas E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Object-Oriented Programming module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/epom-190829181822-thumbnail.jpg?width=120&height=120&fit=bounds" /> E-learning Python for Ocean Mapping (ePOM) project. Complementary slides to the Object-Oriented Programming module (part of the Introduction to Ocean Data Science training). More details at https://www.hydroffice.org/epom

ePOM - Intro to Ocean Data Science - Object-Oriented Programming from Giuseppe Masetti

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0 AusSeabed workshop - Pydro and Hydroffice - Days 2 and 3 /slideshow/ausseabed-workshop-pydro-and-hydroffice-days-2-and-3/155344997 ausseabedworkshop-pydroandhydroffice-days2and3-190713095918
�ݺ�ߣs presented by Giuseppe Masetti (UNH, CCOM/JHC) and Tyanne Faulkes (NOAA, OCS PHB) during the "Effective Seabed Mapping Workflow" Workshop. June 19 and 20, 2019. Canberra, ACT, Australia ]]>
�ݺ�ߣs presented by Giuseppe Masetti (UNH, CCOM/JHC) and Tyanne Faulkes (NOAA, OCS PHB) during the "Effective Seabed Mapping Workflow" Workshop. June 19 and 20, 2019. Canberra, ACT, Australia ]]> Sat, 13 Jul 2019 09:59:18 GMT /slideshow/ausseabed-workshop-pydro-and-hydroffice-days-2-and-3/155344997 giumas@slideshare.net(giumas) AusSeabed workshop - Pydro and Hydroffice - Days 2 and 3 giumas �ݺ�ߣs presented by Giuseppe Masetti (UNH, CCOM/JHC) and Tyanne Faulkes (NOAA, OCS PHB) during the "Effective Seabed Mapping Workflow" Workshop. June 19 and 20, 2019. Canberra, ACT, Australia <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ausseabedworkshop-pydroandhydroffice-days2and3-190713095918-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs presented by Giuseppe Masetti (UNH, CCOM/JHC) and Tyanne Faulkes (NOAA, OCS PHB) during the "Effective Seabed Mapping Workflow" Workshop. June 19 and 20, 2019. Canberra, ACT, Australia

AusSeabed workshop - Pydro and Hydroffice - Days 2 and 3 from Giuseppe Masetti

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0 AusSeabed workshop - Pydro and Hydroffice - Day 1 /slideshow/ausseabed-workshop-pydro-and-hydroffice-day-1/155344501 ausseabedworkshop-pydroandhydroffice-day1-190713094922
�ݺ�ߣs presented by Giuseppe Masetti (UNH, CCOM/JHC) and Tyanne Faulkes (NOAA, OCS PHB) during the "Effective Seabed Mapping Workflow" Workshop. June 18, 2019. Canberra, ACT, Australia]]>
�ݺ�ߣs presented by Giuseppe Masetti (UNH, CCOM/JHC) and Tyanne Faulkes (NOAA, OCS PHB) during the "Effective Seabed Mapping Workflow" Workshop. June 18, 2019. Canberra, ACT, Australia]]> Sat, 13 Jul 2019 09:49:21 GMT /slideshow/ausseabed-workshop-pydro-and-hydroffice-day-1/155344501 giumas@slideshare.net(giumas) AusSeabed workshop - Pydro and Hydroffice - Day 1 giumas �ݺ�ߣs presented by Giuseppe Masetti (UNH, CCOM/JHC) and Tyanne Faulkes (NOAA, OCS PHB) during the "Effective Seabed Mapping Workflow" Workshop. June 18, 2019. Canberra, ACT, Australia <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ausseabedworkshop-pydroandhydroffice-day1-190713094922-thumbnail.jpg?width=120&height=120&fit=bounds" /> �ݺ�ߣs presented by Giuseppe Masetti (UNH, CCOM/JHC) and Tyanne Faulkes (NOAA, OCS PHB) during the "Effective Seabed Mapping Workflow" Workshop. June 18, 2019. Canberra, ACT, Australia

AusSeabed workshop - Pydro and Hydroffice - Day 1 from Giuseppe Masetti

]]> 24738 3 https://cdn.slidesharecdn.com/ss_thumbnails/ausseabedworkshop-pydroandhydroffice-day1-190713094922-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 Hydrographic Survey Validation and Chart Adequacy Assessment Using Automated Solutions /slideshow/hydrographic-survey-validation-and-chart-adequacy-assessment-using-automated-solutions/137775915 ushydro2019masettifaulkeskastrisios-190323015354
Authors: G.Masetti, T.Faulkes, C.Kastrisios The presentation was given at the U.S. Hydro 2019 Conference. Abstract: The rising trend in automation is constantly pushing the hydrographic field toward the exploration and the adoption of more effective approaches for each step of the ping-to-public workflow. However, the large amount of data collected by modern acquisition systems - especially when paired with the force multiplier factor provided by autonomous vessels - conflict with the increasing timeliness expected by today’s final users. Such a situation represents a processing challenge for the largely human-centered solutions that are currently available, and the adoption of automated and semi-automated data quality procedures seems the only scalable and long-term solution to the problem. At the same time, there is an inherent value in propagating the application of such procedures upstream in the survey workflow. In fact, capturing potential issues close (in time and space) to their occurrence has the advantages of reducing the efforts required for their solution and limiting their extent. As such, modern surveys should rely more and more on robust data quality procedures that are applied in near real-time. With the challenge to automate and standardize a large portion of the quality controls used to analyze hydrographic data, NOAA’s Office of Coast Survey and the UNH Center for Coastal and Ocean Mapping have jointly developed (and made publicly available) a pair of software solutions - QC Tools for quality control and CA Tools for chart adequacy - that collect algorithmic implementations for a number of these tasks. Their aim is to verify whether the acquired data satisfy the adopted agency standards (and, in a more general sense, fit for the intended purpose). These standards usually focus on data quality aspects like data density, coverage, and uncertainty evaluation which are largely automated by the developed tools discussed in this paper, leaving to the experienced hydrographer the duty to review the results and supervise the validation process. After an overview of the tools (and the relevant recent improvements driven by field feedback), this work focuses on a new chart adequacy algorithm as well as an experimental approach for bathymetric anomaly detection and classification. A number of examples that use the publicly available solutions in real-world scenarios are also illustrated.]]>
Authors: G.Masetti, T.Faulkes, C.Kastrisios The presentation was given at the U.S. Hydro 2019 Conference. Abstract: The rising trend in automation is constantly pushing the hydrographic field toward the exploration and the adoption of more effective approaches for each step of the ping-to-public workflow. However, the large amount of data collected by modern acquisition systems - especially when paired with the force multiplier factor provided by autonomous vessels - conflict with the increasing timeliness expected by today’s final users. Such a situation represents a processing challenge for the largely human-centered solutions that are currently available, and the adoption of automated and semi-automated data quality procedures seems the only scalable and long-term solution to the problem. At the same time, there is an inherent value in propagating the application of such procedures upstream in the survey workflow. In fact, capturing potential issues close (in time and space) to their occurrence has the advantages of reducing the efforts required for their solution and limiting their extent. As such, modern surveys should rely more and more on robust data quality procedures that are applied in near real-time. With the challenge to automate and standardize a large portion of the quality controls used to analyze hydrographic data, NOAA’s Office of Coast Survey and the UNH Center for Coastal and Ocean Mapping have jointly developed (and made publicly available) a pair of software solutions - QC Tools for quality control and CA Tools for chart adequacy - that collect algorithmic implementations for a number of these tasks. Their aim is to verify whether the acquired data satisfy the adopted agency standards (and, in a more general sense, fit for the intended purpose). These standards usually focus on data quality aspects like data density, coverage, and uncertainty evaluation which are largely automated by the developed tools discussed in this paper, leaving to the experienced hydrographer the duty to review the results and supervise the validation process. After an overview of the tools (and the relevant recent improvements driven by field feedback), this work focuses on a new chart adequacy algorithm as well as an experimental approach for bathymetric anomaly detection and classification. A number of examples that use the publicly available solutions in real-world scenarios are also illustrated.]]> Sat, 23 Mar 2019 01:53:54 GMT /slideshow/hydrographic-survey-validation-and-chart-adequacy-assessment-using-automated-solutions/137775915 giumas@slideshare.net(giumas) Hydrographic Survey Validation and Chart Adequacy Assessment Using Automated Solutions giumas Authors: G.Masetti, T.Faulkes, C.Kastrisios The presentation was given at the U.S. Hydro 2019 Conference. Abstract: The rising trend in automation is constantly pushing the hydrographic field toward the exploration and the adoption of more effective approaches for each step of the ping-to-public workflow. However, the large amount of data collected by modern acquisition systems - especially when paired with the force multiplier factor provided by autonomous vessels - conflict with the increasing timeliness expected by today’s final users. Such a situation represents a processing challenge for the largely human-centered solutions that are currently available, and the adoption of automated and semi-automated data quality procedures seems the only scalable and long-term solution to the problem. At the same time, there is an inherent value in propagating the application of such procedures upstream in the survey workflow. In fact, capturing potential issues close (in time and space) to their occurrence has the advantages of reducing the efforts required for their solution and limiting their extent. As such, modern surveys should rely more and more on robust data quality procedures that are applied in near real-time. With the challenge to automate and standardize a large portion of the quality controls used to analyze hydrographic data, NOAA’s Office of Coast Survey and the UNH Center for Coastal and Ocean Mapping have jointly developed (and made publicly available) a pair of software solutions - QC Tools for quality control and CA Tools for chart adequacy - that collect algorithmic implementations for a number of these tasks. Their aim is to verify whether the acquired data satisfy the adopted agency standards (and, in a more general sense, fit for the intended purpose). These standards usually focus on data quality aspects like data density, coverage, and uncertainty evaluation which are largely automated by the developed tools discussed in this paper, leaving to the experienced hydrographer the duty to review the results and supervise the validation process. After an overview of the tools (and the relevant recent improvements driven by field feedback), this work focuses on a new chart adequacy algorithm as well as an experimental approach for bathymetric anomaly detection and classification. A number of examples that use the publicly available solutions in real-world scenarios are also illustrated. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ushydro2019masettifaulkeskastrisios-190323015354-thumbnail.jpg?width=120&height=120&fit=bounds" /> Authors: G.Masetti, T.Faulkes, C.Kastrisios The presentation was given at the U.S. Hydro 2019 Conference. Abstract: The rising trend in automation is constantly pushing the hydrographic field toward the exploration and the adoption of more effective approaches for each step of the ping-to-public workflow. However, the large amount of data collected by modern acquisition systems - especially when paired with the force multiplier factor provided by autonomous vessels - conflict with the increasing timeliness expected by today’s final users. Such a situation represents a processing challenge for the largely human-centered solutions that are currently available, and the adoption of automated and semi-automated data quality procedures seems the only scalable and long-term solution to the problem. At the same time, there is an inherent value in propagating the application of such procedures upstream in the survey workflow. In fact, capturing potential issues close (in time and space) to their occurrence has the advantages of reducing the efforts required for their solution and limiting their extent. As such, modern surveys should rely more and more on robust data quality procedures that are applied in near real-time. With the challenge to automate and standardize a large portion of the quality controls used to analyze hydrographic data, NOAA’s Office of Coast Survey and the UNH Center for Coastal and Ocean Mapping have jointly developed (and made publicly available) a pair of software solutions - QC Tools for quality control and CA Tools for chart adequacy - that collect algorithmic implementations for a number of these tasks. Their aim is to verify whether the acquired data satisfy the adopted agency standards (and, in a more general sense, fit for the intended purpose). These standards usually focus on data quality aspects like data density, coverage, and uncertainty evaluation which are largely automated by the developed tools discussed in this paper, leaving to the experienced hydrographer the duty to review the results and supervise the validation process. After an overview of the tools (and the relevant recent improvements driven by field feedback), this work focuses on a new chart adequacy algorithm as well as an experimental approach for bathymetric anomaly detection and classification. A number of examples that use the publicly available solutions in real-world scenarios are also illustrated.

Hydrographic Survey Validation and Chart Adequacy Assessment Using Automated Solutions from Giuseppe Masetti

]]> 178760 3 https://cdn.slidesharecdn.com/ss_thumbnails/ushydro2019masettifaulkeskastrisios-190323015354-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 The Open Backscatter Toolchain (OpenBST) project: towards an open-source and metadata-rich modular implementation /slideshow/the-open-backscatter-toolchain-openbst-project-towards-an-opensource-and-metadatarich-modular-implementation/137771694 ushydro2019openbst-190323004941
Authors: G.Masetti, J-M.Augustin, M.Malik, C.Poncelet, X.Lurton, L.Mayer, G.Rice, M.Smith The presentation was given at the U.S. Hydro 2019 Conference. Abstract: Most ocean mapping surveys collect seafloor reflectivity (backscatter) along with bathymetry. While the consistency of bathymetry processed by commonly adopted algorithms is well established, surprisingly large variability is observed between the backscatter mosaics generated by different software packages when processing the same dataset. Such a situation severely limits the use of acoustic backscatter for quantitative analysis (e.g., monitoring seafloor change over time, or remote characterization of seafloor characteristics) and other commonly attempted tasks (e.g., merging mosaics from different origins). Acoustic backscatter processing involves a complex sequence of steps, but inasmuch as commercial software packages mainly provide end-results, comparisons between those results offer little insight into where in the workflow the differences are generated. In addition, preliminary results of a software-inter-comparison working group have shown that each processing algorithm tends to adopt a distinct, unique workflow; this causes large disagreements even in the initial per-beam reflectivity values resulting from differences in basic operations such as snippet averaging and evaluation of flagged beams. Far from ideal, this situation requires a clear shift from the past closed-source approach that has caused it. As such, the Open Backscatter Toolchain (OpenBST) project aims to provide the community with an open-source and metadata-rich modular implementation of a toolchain dedicated to acoustic backscatter processing. The long-term goal is not to create processing tools that would compete with available commercial solutions, but rather a set of open-source, community-vetted, reference algorithms usable by both developers and users for benchmarking their processing algorithms. As a proof-of-concept, we present a prototype implementation with the key elements of the OpenBST approach: • The data conversion from a native acquisition format (i.e., Kongsberg EM Series) to NetCDF-based data structures (components of the eXtensible Sounder Format) better suited to data exploration, processing and metadata coupling. • A processing pipeline constituted by a set of interlocking, task-oriented tools simplifying their substitution with alternative approaches. • The creation of final products (i.e., angular response curves and backscatter mosaics) capturing relevant acquisition and processing metadata.]]>
Authors: G.Masetti, J-M.Augustin, M.Malik, C.Poncelet, X.Lurton, L.Mayer, G.Rice, M.Smith The presentation was given at the U.S. Hydro 2019 Conference. Abstract: Most ocean mapping surveys collect seafloor reflectivity (backscatter) along with bathymetry. While the consistency of bathymetry processed by commonly adopted algorithms is well established, surprisingly large variability is observed between the backscatter mosaics generated by different software packages when processing the same dataset. Such a situation severely limits the use of acoustic backscatter for quantitative analysis (e.g., monitoring seafloor change over time, or remote characterization of seafloor characteristics) and other commonly attempted tasks (e.g., merging mosaics from different origins). Acoustic backscatter processing involves a complex sequence of steps, but inasmuch as commercial software packages mainly provide end-results, comparisons between those results offer little insight into where in the workflow the differences are generated. In addition, preliminary results of a software-inter-comparison working group have shown that each processing algorithm tends to adopt a distinct, unique workflow; this causes large disagreements even in the initial per-beam reflectivity values resulting from differences in basic operations such as snippet averaging and evaluation of flagged beams. Far from ideal, this situation requires a clear shift from the past closed-source approach that has caused it. As such, the Open Backscatter Toolchain (OpenBST) project aims to provide the community with an open-source and metadata-rich modular implementation of a toolchain dedicated to acoustic backscatter processing. The long-term goal is not to create processing tools that would compete with available commercial solutions, but rather a set of open-source, community-vetted, reference algorithms usable by both developers and users for benchmarking their processing algorithms. As a proof-of-concept, we present a prototype implementation with the key elements of the OpenBST approach: • The data conversion from a native acquisition format (i.e., Kongsberg EM Series) to NetCDF-based data structures (components of the eXtensible Sounder Format) better suited to data exploration, processing and metadata coupling. • A processing pipeline constituted by a set of interlocking, task-oriented tools simplifying their substitution with alternative approaches. • The creation of final products (i.e., angular response curves and backscatter mosaics) capturing relevant acquisition and processing metadata.]]> Sat, 23 Mar 2019 00:49:41 GMT /slideshow/the-open-backscatter-toolchain-openbst-project-towards-an-opensource-and-metadatarich-modular-implementation/137771694 giumas@slideshare.net(giumas) The Open Backscatter Toolchain (OpenBST) project: towards an open-source and metadata-rich modular implementation giumas Authors: G.Masetti, J-M.Augustin, M.Malik, C.Poncelet, X.Lurton, L.Mayer, G.Rice, M.Smith The presentation was given at the U.S. Hydro 2019 Conference. Abstract: Most ocean mapping surveys collect seafloor reflectivity (backscatter) along with bathymetry. While the consistency of bathymetry processed by commonly adopted algorithms is well established, surprisingly large variability is observed between the backscatter mosaics generated by different software packages when processing the same dataset. Such a situation severely limits the use of acoustic backscatter for quantitative analysis (e.g., monitoring seafloor change over time, or remote characterization of seafloor characteristics) and other commonly attempted tasks (e.g., merging mosaics from different origins). Acoustic backscatter processing involves a complex sequence of steps, but inasmuch as commercial software packages mainly provide end-results, comparisons between those results offer little insight into where in the workflow the differences are generated. In addition, preliminary results of a software-inter-comparison working group have shown that each processing algorithm tends to adopt a distinct, unique workflow; this causes large disagreements even in the initial per-beam reflectivity values resulting from differences in basic operations such as snippet averaging and evaluation of flagged beams. Far from ideal, this situation requires a clear shift from the past closed-source approach that has caused it. As such, the Open Backscatter Toolchain (OpenBST) project aims to provide the community with an open-source and metadata-rich modular implementation of a toolchain dedicated to acoustic backscatter processing. The long-term goal is not to create processing tools that would compete with available commercial solutions, but rather a set of open-source, community-vetted, reference algorithms usable by both developers and users for benchmarking their processing algorithms. As a proof-of-concept, we present a prototype implementation with the key elements of the OpenBST approach: • The data conversion from a native acquisition format (i.e., Kongsberg EM Series) to NetCDF-based data structures (components of the eXtensible Sounder Format) better suited to data exploration, processing and metadata coupling. • A processing pipeline constituted by a set of interlocking, task-oriented tools simplifying their substitution with alternative approaches. • The creation of final products (i.e., angular response curves and backscatter mosaics) capturing relevant acquisition and processing metadata. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ushydro2019openbst-190323004941-thumbnail.jpg?width=120&height=120&fit=bounds" /> Authors: G.Masetti, J-M.Augustin, M.Malik, C.Poncelet, X.Lurton, L.Mayer, G.Rice, M.Smith The presentation was given at the U.S. Hydro 2019 Conference. Abstract: Most ocean mapping surveys collect seafloor reflectivity (backscatter) along with bathymetry. While the consistency of bathymetry processed by commonly adopted algorithms is well established, surprisingly large variability is observed between the backscatter mosaics generated by different software packages when processing the same dataset. Such a situation severely limits the use of acoustic backscatter for quantitative analysis (e.g., monitoring seafloor change over time, or remote characterization of seafloor characteristics) and other commonly attempted tasks (e.g., merging mosaics from different origins). Acoustic backscatter processing involves a complex sequence of steps, but inasmuch as commercial software packages mainly provide end-results, comparisons between those results offer little insight into where in the workflow the differences are generated. In addition, preliminary results of a software-inter-comparison working group have shown that each processing algorithm tends to adopt a distinct, unique workflow; this causes large disagreements even in the initial per-beam reflectivity values resulting from differences in basic operations such as snippet averaging and evaluation of flagged beams. Far from ideal, this situation requires a clear shift from the past closed-source approach that has caused it. As such, the Open Backscatter Toolchain (OpenBST) project aims to provide the community with an open-source and metadata-rich modular implementation of a toolchain dedicated to acoustic backscatter processing. The long-term goal is not to create processing tools that would compete with available commercial solutions, but rather a set of open-source, community-vetted, reference algorithms usable by both developers and users for benchmarking their processing algorithms. As a proof-of-concept, we present a prototype implementation with the key elements of the OpenBST approach: • The data conversion from a native acquisition format (i.e., Kongsberg EM Series) to NetCDF-based data structures (components of the eXtensible Sounder Format) better suited to data exploration, processing and metadata coupling. • A processing pipeline constituted by a set of interlocking, task-oriented tools simplifying their substitution with alternative approaches. • The creation of final products (i.e., angular response curves and backscatter mosaics) capturing relevant acquisition and processing metadata.

The Open Backscatter Toolchain (OpenBST) project: towards an open-source and metadata-rich modular implementation from Giuseppe Masetti

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0 Pydro & HydrOffice: Open Tools for Ocean Mappers /slideshow/pydro-hydroffice-open-tools-for-ocean-mappers/137026547 ushydro2019pydroandhydroffice-190318185302
Workshop given by Damian Manda (NOAA Office of Coast Survey) and Giuseppe Masetti (UNH Center for Coastal and Ocean Mapping/NOAA-UNH Joint Hydrographic Center) on March 18, 2019 at the US Hydro Conference in Biloxi, MS, USA.]]>
Workshop given by Damian Manda (NOAA Office of Coast Survey) and Giuseppe Masetti (UNH Center for Coastal and Ocean Mapping/NOAA-UNH Joint Hydrographic Center) on March 18, 2019 at the US Hydro Conference in Biloxi, MS, USA.]]> Mon, 18 Mar 2019 18:53:02 GMT /slideshow/pydro-hydroffice-open-tools-for-ocean-mappers/137026547 giumas@slideshare.net(giumas) Pydro & HydrOffice: Open Tools for Ocean Mappers giumas Workshop given by Damian Manda (NOAA Office of Coast Survey) and Giuseppe Masetti (UNH Center for Coastal and Ocean Mapping/NOAA-UNH Joint Hydrographic Center) on March 18, 2019 at the US Hydro Conference in Biloxi, MS, USA. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ushydro2019pydroandhydroffice-190318185302-thumbnail.jpg?width=120&height=120&fit=bounds" /> Workshop given by Damian Manda (NOAA Office of Coast Survey) and Giuseppe Masetti (UNH Center for Coastal and Ocean Mapping/NOAA-UNH Joint Hydrographic Center) on March 18, 2019 at the US Hydro Conference in Biloxi, MS, USA.

Pydro & HydrOffice: Open Tools for Ocean Mappers from Giuseppe Masetti

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0 INMARTECH 2018 - G.Masetti & P.Johnson - Sound Speed Management and Environmental Variability Estimation for Ocean Mapping /slideshow/inmartech-2018-gmasetti-pjohnson-sound-speed-management-and-environmental-variability-estimation-for-ocean-mapping/120063314 masettijohnsoninmartech2018ssmandsmartmap-181020001204
A presentation describing HydrOffice Sound Speed Manager and SmartMap tools in support of the Multibeam training at INMARTECH 2018, Woods Hole, MA.]]>
A presentation describing HydrOffice Sound Speed Manager and SmartMap tools in support of the Multibeam training at INMARTECH 2018, Woods Hole, MA.]]> Sat, 20 Oct 2018 00:12:04 GMT /slideshow/inmartech-2018-gmasetti-pjohnson-sound-speed-management-and-environmental-variability-estimation-for-ocean-mapping/120063314 giumas@slideshare.net(giumas) INMARTECH 2018 - G.Masetti & P.Johnson - Sound Speed Management and Environmental Variability Estimation for Ocean Mapping giumas A presentation describing HydrOffice Sound Speed Manager and SmartMap tools in support of the Multibeam training at INMARTECH 2018, Woods Hole, MA. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/masettijohnsoninmartech2018ssmandsmartmap-181020001204-thumbnail.jpg?width=120&height=120&fit=bounds" /> A presentation describing HydrOffice Sound Speed Manager and SmartMap tools in support of the Multibeam training at INMARTECH 2018, Woods Hole, MA.

INMARTECH 2018 - G.Masetti & P.Johnson - Sound Speed Management and Environmental Variability Estimation for Ocean Mapping from Giuseppe Masetti

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0 Backscatter Working Group Software Inter-comparison Project�Requesting and Comparing Intermediate Backscatter Processing Results /slideshow/backscatter-working-group-software-intercomparison-projectrequesting-and-comparing-intermediate-backscatter-processing-results/118529902 maliketalshallowsurvey2018backscatterintercomparison-181007031038
Backscatter mosaics of the seafloor are now routinely produced from multibeam sonar data, and used in a wide range of marine applications. However, significant differences (up to 5 dB) have been observed between the levels of mosaics produced by different software processing a same dataset. This is a major detriment to several possible uses of backscatter mosaics, including quantitative analysis, monitoring seafloor change over time, and combining mosaics. A recently concluded international Backscatter Working Group (BSWG) identified this issue and recommended that “to check the consistency of the processing results provided by various software suites, initiatives promoting comparative tests on common data sets should be encouraged […]”. However, backscatter data processing is a complex (and often proprietary) sequence of steps, so that simply comparing end-results between software does not provide much information as to the root cause of the differences between results. In order to pinpoint the source(s) of inconsistency between software, it is necessary to understand at which stage(s) of the data processing chain do the differences become substantial. We have invited willing software developers to discuss this framework and collectively adopt a list of intermediate processing steps. We provided a small dataset consisting of various seafloor types surveyed with the same multibeam sonar system, using constant acquisition settings and sea conditions, and have the software developers generate these intermediate processing results, to be eventually compared. If the experiment proves fruitful, we may extend it to more datasets, software and intermediate results. Eventually, software developers may consider making the results from intermediate stages a standard output as well as adhering to a consistent terminology, as advocated by Schimel et al. (2018). To date, the developers of four software (Sonarscope, QPS FMGT, CARIS SIPS, MB Process) have expressed their interest in collaborating on this project.]]>
Backscatter mosaics of the seafloor are now routinely produced from multibeam sonar data, and used in a wide range of marine applications. However, significant differences (up to 5 dB) have been observed between the levels of mosaics produced by different software processing a same dataset. This is a major detriment to several possible uses of backscatter mosaics, including quantitative analysis, monitoring seafloor change over time, and combining mosaics. A recently concluded international Backscatter Working Group (BSWG) identified this issue and recommended that “to check the consistency of the processing results provided by various software suites, initiatives promoting comparative tests on common data sets should be encouraged […]”. However, backscatter data processing is a complex (and often proprietary) sequence of steps, so that simply comparing end-results between software does not provide much information as to the root cause of the differences between results. In order to pinpoint the source(s) of inconsistency between software, it is necessary to understand at which stage(s) of the data processing chain do the differences become substantial. We have invited willing software developers to discuss this framework and collectively adopt a list of intermediate processing steps. We provided a small dataset consisting of various seafloor types surveyed with the same multibeam sonar system, using constant acquisition settings and sea conditions, and have the software developers generate these intermediate processing results, to be eventually compared. If the experiment proves fruitful, we may extend it to more datasets, software and intermediate results. Eventually, software developers may consider making the results from intermediate stages a standard output as well as adhering to a consistent terminology, as advocated by Schimel et al. (2018). To date, the developers of four software (Sonarscope, QPS FMGT, CARIS SIPS, MB Process) have expressed their interest in collaborating on this project.]]> Sun, 07 Oct 2018 03:10:38 GMT /slideshow/backscatter-working-group-software-intercomparison-projectrequesting-and-comparing-intermediate-backscatter-processing-results/118529902 giumas@slideshare.net(giumas) Backscatter Working Group Software Inter-comparison Project�Requesting and Comparing Intermediate Backscatter Processing Results giumas Backscatter mosaics of the seafloor are now routinely produced from multibeam sonar data, and used in a wide range of marine applications. However, significant differences (up to 5 dB) have been observed between the levels of mosaics produced by different software processing a same dataset. This is a major detriment to several possible uses of backscatter mosaics, including quantitative analysis, monitoring seafloor change over time, and combining mosaics. A recently concluded international Backscatter Working Group (BSWG) identified this issue and recommended that “to check the consistency of the processing results provided by various software suites, initiatives promoting comparative tests on common data sets should be encouraged […]”. However, backscatter data processing is a complex (and often proprietary) sequence of steps, so that simply comparing end-results between software does not provide much information as to the root cause of the differences between results. In order to pinpoint the source(s) of inconsistency between software, it is necessary to understand at which stage(s) of the data processing chain do the differences become substantial. We have invited willing software developers to discuss this framework and collectively adopt a list of intermediate processing steps. We provided a small dataset consisting of various seafloor types surveyed with the same multibeam sonar system, using constant acquisition settings and sea conditions, and have the software developers generate these intermediate processing results, to be eventually compared. If the experiment proves fruitful, we may extend it to more datasets, software and intermediate results. Eventually, software developers may consider making the results from intermediate stages a standard output as well as adhering to a consistent terminology, as advocated by Schimel et al. (2018). To date, the developers of four software (Sonarscope, QPS FMGT, CARIS SIPS, MB Process) have expressed their interest in collaborating on this project. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/maliketalshallowsurvey2018backscatterintercomparison-181007031038-thumbnail.jpg?width=120&height=120&fit=bounds" /> Backscatter mosaics of the seafloor are now routinely produced from multibeam sonar data, and used in a wide range of marine applications. However, significant differences (up to 5 dB) have been observed between the levels of mosaics produced by different software processing a same dataset. This is a major detriment to several possible uses of backscatter mosaics, including quantitative analysis, monitoring seafloor change over time, and combining mosaics. A recently concluded international Backscatter Working Group (BSWG) identified this issue and recommended that “to check the consistency of the processing results provided by various software suites, initiatives promoting comparative tests on common data sets should be encouraged […]”. However, backscatter data processing is a complex (and often proprietary) sequence of steps, so that simply comparing end-results between software does not provide much information as to the root cause of the differences between results. In order to pinpoint the source(s) of inconsistency between software, it is necessary to understand at which stage(s) of the data processing chain do the differences become substantial. We have invited willing software developers to discuss this framework and collectively adopt a list of intermediate processing steps. We provided a small dataset consisting of various seafloor types surveyed with the same multibeam sonar system, using constant acquisition settings and sea conditions, and have the software developers generate these intermediate processing results, to be eventually compared. If the experiment proves fruitful, we may extend it to more datasets, software and intermediate results. Eventually, software developers may consider making the results from intermediate stages a standard output as well as adhering to a consistent terminology, as advocated by Schimel et al. (2018). To date, the developers of four software (Sonarscope, QPS FMGT, CARIS SIPS, MB Process) have expressed their interest in collaborating on this project.

Backscatter Working Group Software Inter-comparison Project Requesting and Comparing Intermediate Backscatter Processing Results from Giuseppe Masetti

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0 Shallow Survey 2018 - Applications of Sonar Detection Uncertainty for Survey Quality Control and Data Processing /slideshow/shallow-survey-2018-applications-of-sonar-detection-uncertainty-for-survey-quality-control-and-data-processing/118388084 masettietalss2018sonardetectionuncertainty-181006023923
Authors: Giuseppe Masetti1*, Jean-Marie Augustin2, Xavier Lurton2, Brian R. Calder3 1. CCOM/JHC, University of New Hampshire, Durham, NH, USA, gmasetti@ccom.unh.edu 2. Institut Français de Recherche pour l’Exploitation de la Mer (Ifremer), Brest, France 3. CCOM/JHC, University of New Hampshire, Durham, NH, USA An objective measurement of the bathymetric uncertainty introduced by sonar bottom detection has been proposed (Lurton and Augustin, 2009) to overcome the sonar-specific heuristic solutions proposed by constructors. This approach pairs each sounding with an estimation of sonar detection uncertainty (SDU) based on the width of the signal envelope (amplitude detection) or the noise level of the phase ramp (phase detection), thus capturing the intrinsic quality of the received signal and any applied signal-processing step. Along with the environment characterization and the motion sensor accuracy, the SDU represents a major contributor to the total vertical uncertainty (TVU). As such, the monitoring of the SDU statistics by detection types, acquisition modes, and transmission sectors (when available) provides an effective way to alert the surveyor about ongoing issues in the data collection. It also has potential application in the evaluation of the health status of the sonar - for example, by comparing SDU-derived performance of repeated surveys on the same seafloor area and estimating the uncertainty contributions from environment and motion. Finally, the SDU may be integrated in multiple stages of the data processing workflow, from data pre-filtering to hydrographic uncertainty modeling, up to more advanced applications like hypotheses disambiguation in statistical gridding algorithms (e.g., CUBE). Based on such considerations, we conducted a study to explore possible applications of the estimated SDU values for survey quality control and data processing. The results of the analysis applied to real data – collected using multibeam echosounders from manufacturers who are early adopters of this metric (i.e., Kongsberg Maritime and Teledyne Reson) – provide evidence that SDU is a useful tool for survey monitoring. ]]>
Authors: Giuseppe Masetti1*, Jean-Marie Augustin2, Xavier Lurton2, Brian R. Calder3 1. CCOM/JHC, University of New Hampshire, Durham, NH, USA, gmasetti@ccom.unh.edu 2. Institut Français de Recherche pour l’Exploitation de la Mer (Ifremer), Brest, France 3. CCOM/JHC, University of New Hampshire, Durham, NH, USA An objective measurement of the bathymetric uncertainty introduced by sonar bottom detection has been proposed (Lurton and Augustin, 2009) to overcome the sonar-specific heuristic solutions proposed by constructors. This approach pairs each sounding with an estimation of sonar detection uncertainty (SDU) based on the width of the signal envelope (amplitude detection) or the noise level of the phase ramp (phase detection), thus capturing the intrinsic quality of the received signal and any applied signal-processing step. Along with the environment characterization and the motion sensor accuracy, the SDU represents a major contributor to the total vertical uncertainty (TVU). As such, the monitoring of the SDU statistics by detection types, acquisition modes, and transmission sectors (when available) provides an effective way to alert the surveyor about ongoing issues in the data collection. It also has potential application in the evaluation of the health status of the sonar - for example, by comparing SDU-derived performance of repeated surveys on the same seafloor area and estimating the uncertainty contributions from environment and motion. Finally, the SDU may be integrated in multiple stages of the data processing workflow, from data pre-filtering to hydrographic uncertainty modeling, up to more advanced applications like hypotheses disambiguation in statistical gridding algorithms (e.g., CUBE). Based on such considerations, we conducted a study to explore possible applications of the estimated SDU values for survey quality control and data processing. The results of the analysis applied to real data – collected using multibeam echosounders from manufacturers who are early adopters of this metric (i.e., Kongsberg Maritime and Teledyne Reson) – provide evidence that SDU is a useful tool for survey monitoring. ]]> Sat, 06 Oct 2018 02:39:23 GMT /slideshow/shallow-survey-2018-applications-of-sonar-detection-uncertainty-for-survey-quality-control-and-data-processing/118388084 giumas@slideshare.net(giumas) Shallow Survey 2018 - Applications of Sonar Detection Uncertainty for Survey Quality Control and Data Processing giumas Authors: Giuseppe Masetti1*, Jean-Marie Augustin2, Xavier Lurton2, Brian R. Calder3 1. CCOM/JHC, University of New Hampshire, Durham, NH, USA, gmasetti@ccom.unh.edu 2. Institut Français de Recherche pour l’Exploitation de la Mer (Ifremer), Brest, France 3. CCOM/JHC, University of New Hampshire, Durham, NH, USA An objective measurement of the bathymetric uncertainty introduced by sonar bottom detection has been proposed (Lurton and Augustin, 2009) to overcome the sonar-specific heuristic solutions proposed by constructors. This approach pairs each sounding with an estimation of sonar detection uncertainty (SDU) based on the width of the signal envelope (amplitude detection) or the noise level of the phase ramp (phase detection), thus capturing the intrinsic quality of the received signal and any applied signal-processing step. Along with the environment characterization and the motion sensor accuracy, the SDU represents a major contributor to the total vertical uncertainty (TVU). As such, the monitoring of the SDU statistics by detection types, acquisition modes, and transmission sectors (when available) provides an effective way to alert the surveyor about ongoing issues in the data collection. It also has potential application in the evaluation of the health status of the sonar - for example, by comparing SDU-derived performance of repeated surveys on the same seafloor area and estimating the uncertainty contributions from environment and motion. Finally, the SDU may be integrated in multiple stages of the data processing workflow, from data pre-filtering to hydrographic uncertainty modeling, up to more advanced applications like hypotheses disambiguation in statistical gridding algorithms (e.g., CUBE). Based on such considerations, we conducted a study to explore possible applications of the estimated SDU values for survey quality control and data processing. The results of the analysis applied to real data – collected using multibeam echosounders from manufacturers who are early adopters of this metric (i.e., Kongsberg Maritime and Teledyne Reson) – provide evidence that SDU is a useful tool for survey monitoring. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/masettietalss2018sonardetectionuncertainty-181006023923-thumbnail.jpg?width=120&height=120&fit=bounds" /> Authors: Giuseppe Masetti1*, Jean-Marie Augustin2, Xavier Lurton2, Brian R. Calder3 1. CCOM/JHC, University of New Hampshire, Durham, NH, USA, gmasetti@ccom.unh.edu 2. Institut Français de Recherche pour l’Exploitation de la Mer (Ifremer), Brest, France 3. CCOM/JHC, University of New Hampshire, Durham, NH, USA An objective measurement of the bathymetric uncertainty introduced by sonar bottom detection has been proposed (Lurton and Augustin, 2009) to overcome the sonar-specific heuristic solutions proposed by constructors. This approach pairs each sounding with an estimation of sonar detection uncertainty (SDU) based on the width of the signal envelope (amplitude detection) or the noise level of the phase ramp (phase detection), thus capturing the intrinsic quality of the received signal and any applied signal-processing step. Along with the environment characterization and the motion sensor accuracy, the SDU represents a major contributor to the total vertical uncertainty (TVU). As such, the monitoring of the SDU statistics by detection types, acquisition modes, and transmission sectors (when available) provides an effective way to alert the surveyor about ongoing issues in the data collection. It also has potential application in the evaluation of the health status of the sonar - for example, by comparing SDU-derived performance of repeated surveys on the same seafloor area and estimating the uncertainty contributions from environment and motion. Finally, the SDU may be integrated in multiple stages of the data processing workflow, from data pre-filtering to hydrographic uncertainty modeling, up to more advanced applications like hypotheses disambiguation in statistical gridding algorithms (e.g., CUBE). Based on such considerations, we conducted a study to explore possible applications of the estimated SDU values for survey quality control and data processing. The results of the analysis applied to real data – collected using multibeam echosounders from manufacturers who are early adopters of this metric (i.e., Kongsberg Maritime and Teledyne Reson) – provide evidence that SDU is a useful tool for survey monitoring.

Shallow Survey 2018 - Applications of Sonar Detection Uncertainty for Survey Quality Control and Data Processing from Giuseppe Masetti

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0 Bathymetric Attributed Grid (BAG) format /slideshow/bathymetric-attributed-grid-bag-format/105374127 bagformat-180711155743
A presentation about the BAG format that was given at Ifremer. Brest, 20 June 2018.]]>
A presentation about the BAG format that was given at Ifremer. Brest, 20 June 2018.]]> Wed, 11 Jul 2018 15:57:43 GMT /slideshow/bathymetric-attributed-grid-bag-format/105374127 giumas@slideshare.net(giumas) Bathymetric Attributed Grid (BAG) format giumas A presentation about the BAG format that was given at Ifremer. Brest, 20 June 2018. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/bagformat-180711155743-thumbnail.jpg?width=120&height=120&fit=bounds" /> A presentation about the BAG format that was given at Ifremer. Brest, 20 June 2018.

Bathymetric Attributed Grid (BAG) format from Giuseppe Masetti

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0 https://cdn.slidesharecdn.com/profile-photo-giumas-48x48.jpg?cb=1744463578 ccom.unh.edu/user/gmasetti https://cdn.slidesharecdn.com/ss_thumbnails/ushydro2025hssdmattwilson-250412131419-77ada7eb-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/us-hydro-2025-new-hydrographic-survey-specifications-updates-and-enhancements/277855217 US Hydro 2025 - New Hy... https://cdn.slidesharecdn.com/ss_thumbnails/msmithcanadianhydro2020v1-200228150235-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/open-backscatter-toolchain-openbst-project-a-communityvetted-workflow-for-backscatter-processing/229396305 Open Backscatter Toolc... https://cdn.slidesharecdn.com/ss_thumbnails/chc2020epommasettietal-200227183722-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/elearning-python-for-ocean-mapping-empowering-the-next-generation-of-ocean-mappers-with-effective-programming-skills/229332713 e-learning Python for ...