Transfer learning reconstructs submarine topography for global mid-ocean ridges
Mid-ocean ridges are unique, tectonically active geographical units on Earth that profoundly control the ocean environment and dynamics at the global scale. However, high-resolution topographic data from mid-ocean ridges are rarely available due to the difficulty in detecting ocean floors, which fur...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843224005387 |
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| author | Yinghui Jiang Sijin Li Yanzi Yan Bingqing Sun Josef Strobl Liyang Xiong |
| author_facet | Yinghui Jiang Sijin Li Yanzi Yan Bingqing Sun Josef Strobl Liyang Xiong |
| author_sort | Yinghui Jiang |
| collection | DOAJ |
| description | Mid-ocean ridges are unique, tectonically active geographical units on Earth that profoundly control the ocean environment and dynamics at the global scale. However, high-resolution topographic data from mid-ocean ridges are rarely available due to the difficulty in detecting ocean floors, which further limits ocean research at the global scale. Here, we divide the global mid-ocean ridge system into 2805 tiles and reconstruct their high-resolution topography by using a transfer learning approach with freely available low-resolution digital elevation models (DEMs) and limited high-resolution DEMs. A high-frequency terrain feature-based deep residual network is proposed to generate high-resolution global mid-ocean ridge DEMs. In this network, topographic knowledge related to mid-ocean ridges is integrated and quantified to improve the learning efficiency and reconstruction quality of the network. A series of verifications and evaluations demonstrate the reliability of reconstructed topographies for submarine topography research. We observe that reconstructed topography can achieve good environmental understanding and information acquisition in the global mid-ocean ridge range. We find that the complexity of the previous terrain environment is underestimated by 26.63% in terms of the slope gradient and by 14.95% in terms of terrain relief, while a 101.10% information improvement can be obtained for the reconstructed topography. The reconstructed topography indicates that diverse and intricate topographical environments of mid-ocean ridges exist among different ocean regions. The proposed transfer learning method for reconstructing high-resolution mid-ocean ridge topographies is valuable and can be utilized for reconstructing information in regions that are difficult to observe directly and lack sufficient data. |
| format | Article |
| id | doaj-art-5d874b7f19a549fdb2da5be2c690119c |
| institution | Kabale University |
| issn | 1569-8432 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj-art-5d874b7f19a549fdb2da5be2c690119c2024-11-16T05:10:07ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322024-11-01134104182Transfer learning reconstructs submarine topography for global mid-ocean ridgesYinghui Jiang0Sijin Li1Yanzi Yan2Bingqing Sun3Josef Strobl4Liyang Xiong5School of Geography, Nanjing Normal University, Nanjing 210023, ChinaSchool of Geography, Nanjing Normal University, Nanjing 210023, ChinaInstitute of Carbon Neutrality, Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China; Department of Physical Geography and Ecosystem Sciences, Lund University, Lund, SwedenHonors College, Nanjing Normal University, Nanjing 210023, ChinaDepartment of Geoinformatics – Z_GIS, University of Salzburg, Salzburg 5020, AustriaSchool of Geography, Nanjing Normal University, Nanjing 210023, China; Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210023, China; Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China; Corresponding author at: School of Geography, Nanjing Normal University, Nanjing 210023, China.Mid-ocean ridges are unique, tectonically active geographical units on Earth that profoundly control the ocean environment and dynamics at the global scale. However, high-resolution topographic data from mid-ocean ridges are rarely available due to the difficulty in detecting ocean floors, which further limits ocean research at the global scale. Here, we divide the global mid-ocean ridge system into 2805 tiles and reconstruct their high-resolution topography by using a transfer learning approach with freely available low-resolution digital elevation models (DEMs) and limited high-resolution DEMs. A high-frequency terrain feature-based deep residual network is proposed to generate high-resolution global mid-ocean ridge DEMs. In this network, topographic knowledge related to mid-ocean ridges is integrated and quantified to improve the learning efficiency and reconstruction quality of the network. A series of verifications and evaluations demonstrate the reliability of reconstructed topographies for submarine topography research. We observe that reconstructed topography can achieve good environmental understanding and information acquisition in the global mid-ocean ridge range. We find that the complexity of the previous terrain environment is underestimated by 26.63% in terms of the slope gradient and by 14.95% in terms of terrain relief, while a 101.10% information improvement can be obtained for the reconstructed topography. The reconstructed topography indicates that diverse and intricate topographical environments of mid-ocean ridges exist among different ocean regions. The proposed transfer learning method for reconstructing high-resolution mid-ocean ridge topographies is valuable and can be utilized for reconstructing information in regions that are difficult to observe directly and lack sufficient data.http://www.sciencedirect.com/science/article/pii/S1569843224005387Submarine topography modelingTransfer learningGlobal mid-ocean ridgesDEM super-resolution |
| spellingShingle | Yinghui Jiang Sijin Li Yanzi Yan Bingqing Sun Josef Strobl Liyang Xiong Transfer learning reconstructs submarine topography for global mid-ocean ridges International Journal of Applied Earth Observations and Geoinformation Submarine topography modeling Transfer learning Global mid-ocean ridges DEM super-resolution |
| title | Transfer learning reconstructs submarine topography for global mid-ocean ridges |
| title_full | Transfer learning reconstructs submarine topography for global mid-ocean ridges |
| title_fullStr | Transfer learning reconstructs submarine topography for global mid-ocean ridges |
| title_full_unstemmed | Transfer learning reconstructs submarine topography for global mid-ocean ridges |
| title_short | Transfer learning reconstructs submarine topography for global mid-ocean ridges |
| title_sort | transfer learning reconstructs submarine topography for global mid ocean ridges |
| topic | Submarine topography modeling Transfer learning Global mid-ocean ridges DEM super-resolution |
| url | http://www.sciencedirect.com/science/article/pii/S1569843224005387 |
| work_keys_str_mv | AT yinghuijiang transferlearningreconstructssubmarinetopographyforglobalmidoceanridges AT sijinli transferlearningreconstructssubmarinetopographyforglobalmidoceanridges AT yanziyan transferlearningreconstructssubmarinetopographyforglobalmidoceanridges AT bingqingsun transferlearningreconstructssubmarinetopographyforglobalmidoceanridges AT josefstrobl transferlearningreconstructssubmarinetopographyforglobalmidoceanridges AT liyangxiong transferlearningreconstructssubmarinetopographyforglobalmidoceanridges |