{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T14:46:09Z","timestamp":1768747569740,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T00:00:00Z","timestamp":1697068800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62125102"],"award-info":[{"award-number":["62125102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The prediction of a tropical cyclone\u2019s trajectory is crucial for ensuring marine safety and promoting economic growth. Previous approaches to this task have been broadly categorized as either numerical or statistical methods, with the former being computationally expensive. Among the latter, multilayer perceptron (MLP)-based methods have been found to be simple but lacking in time series capabilities, while recurrent neural network (RNN)-based methods excel at processing time series data but do not integrate external information. Recent works have attempted to enhance prediction performance by simultaneously utilizing both time series and meteorological field data through feature fusion. However, these approaches have relatively simplistic methods for data fusion and do not fully explore the correlations between different modalities. To address these limitations, we propose a systematic solution called TC-TrajGRU for predicting tropical cyclone tracks. Our approach improves upon existing methods in two main ways. Firstly, we introduce a Spatial Alignment Feature Fusion (SAFF) module to address feature misalignment issues in different dimensions. Secondly, our Track-to-Velocity (T2V) module leverages time series differences to integrate external information. Our experiments demonstrate that our approach yields highly accurate predictions comparable to the official optimal forecast for a 12 h period.<\/jats:p>","DOI":"10.3390\/rs15204938","type":"journal-article","created":{"date-parts":[[2023,10,12]],"date-time":"2023-10-12T12:46:13Z","timestamp":1697114773000},"page":"4938","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Spatio-Temporal Alignment and Track-To-Velocity Module for Tropical Cyclone Forecast"],"prefix":"10.3390","volume":"15","author":[{"given":"Xiaoyi","family":"Geng","sequence":"first","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"Beijing Key Laboratory of Digital Media, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"given":"Zili","family":"Liu","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"Beijing Key Laboratory of Digital Media, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, Beihang University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4772-3172","authenticated-orcid":false,"given":"Zhenwei","family":"Shi","sequence":"additional","affiliation":[{"name":"Image Processing Center, School of Astronautics, Beihang University, Beijing 100191, China"},{"name":"Beijing Key Laboratory of Digital Media, Beihang University, Beijing 100191, China"},{"name":"State Key Laboratory of Virtual Reality Technology and Systems, School of Astronautics, Beihang University, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,12]]},"reference":[{"key":"ref_1","unstructured":"Pickle, J.D. 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