The prediction of ship trajectories is a growing field of study in artificial intelligence. Traditional methods rely on the use of LSTM, GRU networks, and even Transformer architectures for the prediction of spatio-temporal series. This study proposes a viable alternative for predicting these trajectories using only GNSS positions. It considers this spatio-temporal problem as a natural language processing problem. The latitude/longitude coordinates of AIS messages are transformed into cell identifiers using the H3 index. Thanks to the pseudo-octal representation, it becomes easier for language models to learn the spatial hierarchy of the H3 index. The method is compared with a classical Kalman filter, widely used in the maritime domain, and introduces the Fréchet distance as the main evaluation metric. We show that it is possible to predict ship trajectories quite precisely up to 8 hours ahead with 30 minutes of context, using solely GNSS positions, without relying on any additional information such as speed, course, or external conditions - unlike many traditional methods. We demonstrate that this alternative works well enough to predict trajectories worldwide.
Towards Long-Horizon Vessel Trajectory and Destination Forecasting with Reasoning Large Language Models
Institute of High Performance Computing (IHPC), A*STAR, Singapore（新加坡科技研究局高性能计算研究所）；The Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University（同济大学道路与交通工程教育部重点实验室）；Meituan Inc., Shenzhen, China（美团（深圳））；Centre for Frontier AI Research (CFAR), A*STAR, Singapore（新加坡科技研究局前沿人工智能研究中心）；Nankai University（南开大学）；School of Artificial Intelligence, Jilin University（吉林大学人工智能学院）
Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models
Department of Computer Science, Memorial University of Newfoundland（纪念大学计算机科学系）；Industrial Engineering Department, Dalhousie University（达尔豪斯大学工业工程系）；Institute for Big Data Analytics, Dalhousie University（达尔豪斯大学大数据分析研究所）；Institute of Computer Science, Polish Academy of Sciences（波兰科学院计算机科学研究所）；Department of Computer Science and Media Technology, Linnaeus University（林纳大学计算机科学与媒体技术系）
FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化院）；Shanghai Innovation Institute（上海创新研究院）；School of Marine Science and Technology,Tianjin University（天津大学海洋科学与技术学院）
Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting
Centro de Tecnologías de la Imagen (CTIM)（图像技术中心）；Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES)（网络、企业与社会大学研究所）；University of Las Palmas de Gran Canaria, Spain（大加那利岛大学）；Oceanografía Física y Geofísica Aplicada (OFYGA)（应用物理海洋学与地球物理研究所）；Instituto Universitario de Investigación en Acuicultura Sostenible y Ecosistemas Marinos (ECOAQUA)（可持续水产养殖与海洋生态系统大学研究所）
Data-Driven Uncertainty-Aware Forecasting of Sea Ice Conditions in the Gulf of Ob Based on Satellite Radar Imagery
Applied AI Center, Skolkovo Institute of Science and Technology, Moscow, Russia（斯克尔科沃科学与技术研究所应用人工智能中心）；Moscow Institute of Physics and Technology, Dolgoprudny, Russia（莫斯科物理技术学院）；Autonomous Non-Profit Organization Artificial Intelligence Research Institute (AIRI), Moscow, Russia（自主非营利组织人工智能研究 institute（AIRI））
Multi-vessel Interaction-Aware Trajectory Prediction and Collision Risk Assessment
Faculty of Computer Science, Dalhousie University, Halifax, Canada（计算机科学学院，达尔豪斯大学，加拿大哈利法克斯）；Computer Science Department, University of São Paulo, São Carlos, Brazil（计算机科学系，圣保罗大学，巴西萨昂卡尔）
Forecasting precipitation in the Arctic using probabilistic machine learning informed by causal climate drivers
Department of Science and Engineering, Sorbonne University, Abu Dhabi, United Arab Emirates（Sorbonne大学科学与工程系）；Physics and Applied Mathematics Unit, Indian Statistical Institute, Kolkata 700108, India（印度统计研究院应用数学与物理单元）；Sorbonne Center for Artificial Intelligence, Sorbonne University, Paris, 75005, France（Sorbonne大学人工智能中心）；Indian Institute of Technology Gandhinagar, Gandhinagar 382355, India（印度理工学院冈丁加尔分校）；National Centre for Polar and Ocean Research, Ministry of Earth Sciences, Vasco da Gama, 403804, India（国家极地与海洋研究中心）；Center for Computational Natural Science and Bioinformatics, International Institute of Information Technology, Gachibowli, Hyderabad-500032, India（国际信息技术研究所计算自然科学与生物信息中心）；Department of Dynamics, Lodz University of Technology, Stefanowskiego 1/15, 90–537 Lodz, Poland（Lodz技术大学动力学系）；Centre for Mathematical Biology and Ecology, Department of Mathematics, Jadavpur University, Kolkata 700032, India（贾瓦德普大学数学生物学与生态中心）
Improving Significant Wave Height Prediction Using Chronos Models
School of Hydraulic and Civil Engineering, Ludong University（鲁东大学水利与土木工程学院）；Institute of Coastal Research, Ludong University（鲁东大学海岸研究所）；Navigation College, Dalian Maritime University（大连海事大学航海学院）；Centre for Ports and Maritime Safety, Dalian Maritime University（大连海事大学港口与海事安全中心）
