Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data
The potential of Arctic routes (ARs) has attracted global attention, and exploiting the Arctic has become an important strategy for many countries. However, there are still some challenges for ships sailing in Arctic ice zones, including harsh marine environments and the insufficient service capacit...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17538947.2022.2126016 |
| _version_ | 1797678518064119808 |
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| author | Adan Wu Tao Che Xin Li Xiaowen Zhu |
| author_facet | Adan Wu Tao Che Xin Li Xiaowen Zhu |
| author_sort | Adan Wu |
| collection | DOAJ |
| description | The potential of Arctic routes (ARs) has attracted global attention, and exploiting the Arctic has become an important strategy for many countries. However, there are still some challenges for ships sailing in Arctic ice zones, including harsh marine environments and the insufficient service capacity of sea ice information service systems. To better understand the route changes in the Arctic and extract real-time ship navigation routes, we developed an online interactive route planning system (RouteView) for ships sailing in the Arctic based on big Earth data. RouteView includes two main features: (1) an online calculation interface is provided for optimal routes along the Arctic Northeast Passage (NEP) 60 days into the future by utilizing reinforcement learning (RL) based on sea ice and meteorological data, and (2) an online ice-water classification is established based on synthetic aperture radar (SAR) data and deep learning to help users extract the sea ice distribution in real time. This work can potentially enhance the safety of shipping navigation along the NEP and improve information extraction methods for ARs. |
| first_indexed | 2024-03-11T23:00:58Z |
| format | Article |
| id | doaj.art-db56ffe0c1e44d9488184e671101474c |
| institution | Directory Open Access Journal |
| issn | 1753-8947 1753-8955 |
| language | English |
| last_indexed | 2024-03-11T23:00:58Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj.art-db56ffe0c1e44d9488184e671101474c2023-09-21T14:57:11ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552022-12-011511588161310.1080/17538947.2022.21260162126016Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth dataAdan Wu0Tao Che1Xin Li2Xiaowen Zhu3Chinese Academy of SciencesChinese Academy of SciencesChinese Academy of SciencesChinese Academy of SciencesThe potential of Arctic routes (ARs) has attracted global attention, and exploiting the Arctic has become an important strategy for many countries. However, there are still some challenges for ships sailing in Arctic ice zones, including harsh marine environments and the insufficient service capacity of sea ice information service systems. To better understand the route changes in the Arctic and extract real-time ship navigation routes, we developed an online interactive route planning system (RouteView) for ships sailing in the Arctic based on big Earth data. RouteView includes two main features: (1) an online calculation interface is provided for optimal routes along the Arctic Northeast Passage (NEP) 60 days into the future by utilizing reinforcement learning (RL) based on sea ice and meteorological data, and (2) an online ice-water classification is established based on synthetic aperture radar (SAR) data and deep learning to help users extract the sea ice distribution in real time. This work can potentially enhance the safety of shipping navigation along the NEP and improve information extraction methods for ARs.http://dx.doi.org/10.1080/17538947.2022.2126016arctic northeast passageinformation extraction systemreinforcement learningu-netroute planningice-water classification |
| spellingShingle | Adan Wu Tao Che Xin Li Xiaowen Zhu Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data International Journal of Digital Earth arctic northeast passage information extraction system reinforcement learning u-net route planning ice-water classification |
| title | Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data |
| title_full | Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data |
| title_fullStr | Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data |
| title_full_unstemmed | Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data |
| title_short | Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data |
| title_sort | routeview an intelligent route planning system for ships sailing through arctic ice zones based on big earth data |
| topic | arctic northeast passage information extraction system reinforcement learning u-net route planning ice-water classification |
| url | http://dx.doi.org/10.1080/17538947.2022.2126016 |
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