Routeview: an intelligent route planning system for ships sailing through Arctic ice zones based on big Earth data. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Wu, Adan; Che, Tao; Li, Xin; Zhu, Xiaowen
• Is Part Of: International journal of digital earth. Volume 15:Issue 1(2022)
• Page Start: 1588
• Page End: 1613
• Publication Date: 2022-12-31
• Subjects: Arctic northeast passage -- information extraction system -- reinforcement learning -- U-Net -- route planning -- ice-water classification; Geographic information systems -- Periodicals; Sustainable development -- Information technology -- Periodicals; Social planning -- Information technology -- Periodicals; 910.285
• DOI: 10.1080/17538947.2022.2126016
• Languages: English
• ISSNs: 1753-8947

Abstract

ABSTRACT : 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.