2:30 PM - 2:45 PM
[MGI30-04] Seabed2030 project in the frame work of the UN Decade of Ocean Science
Keywords:Seabed2030 project, GEBCO project, UN Decade of Ocean Science, seafloor bathymetry
The Japan Agency for Marine-Earth Science and Technology has launched one of its interdisciplinary programs, Mathematical Seafloor Geomorphology Science, in October 2019 (http://www.jamstec.go.jp/msg/e/), and is collaborating with the machine learning field. The progress of the program and its future prospects were reported at JpGU2021. We would like to introduce the progress and future prospects of the program in this session.
One of the major tasks of the Mathematical Seafloor Geomorphology Project is the item contributing to the growing demand for high-resolution bathymetry information. is the annual updating of bathymetric maps. This is a joint project of the International Hydrographic Organization (IHO) and the UNESCO Intergovernmental Oceanographic Commission (UNESCO-IOC) called GEBCO (General Bathymetric Chart of the Oceans) to produce bathymetric maps of the entire world and to help standardize the names of bathymetry In 2017, the Nippon Foundation funded "The NIPPON FOUNDATION - GEBCO Seabed2030 Project" (hereafter referred to as Seabed2030), a proposal to create a 100 m x 100 m grid resolution map of the global seafloor by 2030, based on water depths up to 1500 m.
One approach is to provide measured data from JAMSTEC fleet vessels.
The British Oceanographic Data Centre, UK is the global center, under which the following four regional centers collect data for their respective areas of responsibility.
1. the Atlantic and Indian Oceans: Lamont-Doherty Earth Observatory of Columbia University, USA
2. Arctic and North Pacific Regional Center (co-sponsored by the University of New Hampshire and Stockholm University)
3. the Southern Ocean (Alfred Wegener Institute, Germany)
4. the South and West Pacific Oceans (National Institute of Water and Atmospheric Research, New Zealand).
In addition, the International Hydrographic Association (IHO) Digital Bathymetry Data Center has a digital archive of global bathymetry data and a central repository function.
JAMSTEC provides data not only for NIWA, but also for other oceanographic regions. The QAQC of data acquired by Marine observation technicians and the management system established by the Research Data Publication Group are regarded as a model for other regional centers around the world.
Regular annual meetings with these agencies, thematic subcommittees, and public lectures have been held. The remaining draft plan through 2030 and crowdsourced deployment through outreach activities are also introduced, and together with the plan, the project calls for cooperation in providing data and other assistance to oceanographic research stakeholders. The Ocean Shot project was launched under the auspices of the Sasakawa Peace Foundation as a new global-scale next-generation mapping collaboration, led by Seabed2030 and involving the Scripps Institution of Oceanography, Stockholm University, the University of New Hampshire, and others, Seabed2030 and JAMSTEC are principan institutions od the project.
As for domestic collaboration, we have concluded a collaboration agreement with the Japan Coast Guard to share information on seafloor topographic data, and are contributing to Seabed2030 while taking advantage of the characteristics of each organization.
As of the press release of May 2023, measured data still accounts for only 25% of the total oceanographic data, and needed to fill the gap the maps. In order to realize SDG 14 (Oceans), one of them, efforts to focus on the ocean sector, which still has many unknowns, are being promoted both domestically and internationally. This is an opportunity for related organizations to cooperate with each other and promote the progress of the UN Decade of Ocean Sciences.
As a new approach to fill the gap in bathymetric maps, the Mathematical Bathymetry Project has made significant achievements by launching items that sublimate from low-resolution to high-resolution data through machine learning. For example, Hidaka et al. (2021) compared several image-super-resolution techniques (ISR) using deep learning to contribute to ocean areas where only low-resolution bathymetric data is available, and examined their applicability. Murakami and Matsuoka et al. (submitted) have further developed their ISR and succeeded in comparing similar bathymetric data using bathymetric data from Okinotorishima Islands. The versatility of the ISR is demonstrated for data on seafloor geometries with different characteristics, such as trough-axis basins and petit-spot origin seamounts on the ocean floor. The strategy is to fill the gap in global bathymetry data by 2030 with such a two-axis approach.
One of the major tasks of the Mathematical Seafloor Geomorphology Project is the item contributing to the growing demand for high-resolution bathymetry information. is the annual updating of bathymetric maps. This is a joint project of the International Hydrographic Organization (IHO) and the UNESCO Intergovernmental Oceanographic Commission (UNESCO-IOC) called GEBCO (General Bathymetric Chart of the Oceans) to produce bathymetric maps of the entire world and to help standardize the names of bathymetry In 2017, the Nippon Foundation funded "The NIPPON FOUNDATION - GEBCO Seabed2030 Project" (hereafter referred to as Seabed2030), a proposal to create a 100 m x 100 m grid resolution map of the global seafloor by 2030, based on water depths up to 1500 m.
One approach is to provide measured data from JAMSTEC fleet vessels.
The British Oceanographic Data Centre, UK is the global center, under which the following four regional centers collect data for their respective areas of responsibility.
1. the Atlantic and Indian Oceans: Lamont-Doherty Earth Observatory of Columbia University, USA
2. Arctic and North Pacific Regional Center (co-sponsored by the University of New Hampshire and Stockholm University)
3. the Southern Ocean (Alfred Wegener Institute, Germany)
4. the South and West Pacific Oceans (National Institute of Water and Atmospheric Research, New Zealand).
In addition, the International Hydrographic Association (IHO) Digital Bathymetry Data Center has a digital archive of global bathymetry data and a central repository function.
JAMSTEC provides data not only for NIWA, but also for other oceanographic regions. The QAQC of data acquired by Marine observation technicians and the management system established by the Research Data Publication Group are regarded as a model for other regional centers around the world.
Regular annual meetings with these agencies, thematic subcommittees, and public lectures have been held. The remaining draft plan through 2030 and crowdsourced deployment through outreach activities are also introduced, and together with the plan, the project calls for cooperation in providing data and other assistance to oceanographic research stakeholders. The Ocean Shot project was launched under the auspices of the Sasakawa Peace Foundation as a new global-scale next-generation mapping collaboration, led by Seabed2030 and involving the Scripps Institution of Oceanography, Stockholm University, the University of New Hampshire, and others, Seabed2030 and JAMSTEC are principan institutions od the project.
As for domestic collaboration, we have concluded a collaboration agreement with the Japan Coast Guard to share information on seafloor topographic data, and are contributing to Seabed2030 while taking advantage of the characteristics of each organization.
As of the press release of May 2023, measured data still accounts for only 25% of the total oceanographic data, and needed to fill the gap the maps. In order to realize SDG 14 (Oceans), one of them, efforts to focus on the ocean sector, which still has many unknowns, are being promoted both domestically and internationally. This is an opportunity for related organizations to cooperate with each other and promote the progress of the UN Decade of Ocean Sciences.
As a new approach to fill the gap in bathymetric maps, the Mathematical Bathymetry Project has made significant achievements by launching items that sublimate from low-resolution to high-resolution data through machine learning. For example, Hidaka et al. (2021) compared several image-super-resolution techniques (ISR) using deep learning to contribute to ocean areas where only low-resolution bathymetric data is available, and examined their applicability. Murakami and Matsuoka et al. (submitted) have further developed their ISR and succeeded in comparing similar bathymetric data using bathymetric data from Okinotorishima Islands. The versatility of the ISR is demonstrated for data on seafloor geometries with different characteristics, such as trough-axis basins and petit-spot origin seamounts on the ocean floor. The strategy is to fill the gap in global bathymetry data by 2030 with such a two-axis approach.