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Natural language has become the preferred way to express complex query requirements and intents, due to its intuitiveness and universal applicability. In light of this, we propose a natural language-based AIS trajectory query approach using large language models. Firstly, trajectory textualization was designed to convert the time sequences of trajectories into semantic descriptions by segmenting AIS trajectories, extracting semantics, and constructing trajectory documents. Then, the semantic trajectory querying was completed by rewriting queries, retrieving AIS trajectories, and generating answers. Finally, comparative experiments were conducted to highlight the improvements in accuracy and relevance achieved by our proposed method over traditional approaches. Furthermore, a human study demonstrated the user-friendly interaction experience enabled by our approach. Additionally, we conducted an ablation study to illustrate the significant contributions of each module within our framework. The results demonstrate that our approach effectively bridges the gap between AIS trajectories and natural language query intents, offering an intuitive, user-friendly, and accessible solution for domain experts and novices.<\/jats:p>","DOI":"10.3390\/ijgi14050204","type":"journal-article","created":{"date-parts":[[2025,5,16]],"date-time":"2025-05-16T09:30:38Z","timestamp":1747387838000},"page":"204","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Natural Language-Based Automatic Identification System Trajectory Query Approach Using Large Language Models"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6678-4168","authenticated-orcid":false,"given":"Xuan","family":"Guo","sequence":"first","affiliation":[{"name":"The Key Laboratory of Smart Earth, Beijing 100094, China"},{"name":"State Key Laboratory of Spatial Datum, Xi\u2019an 710054, China"},{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shutong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinxue","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanyu","family":"Bi","sequence":"additional","affiliation":[{"name":"School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohui","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Data and Target Engineering, Information Engineering University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3211-5342","authenticated-orcid":false,"given":"Junnan","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Spatial Datum, Xi\u2019an 710054, China"},{"name":"School of Earth Science and Technology, Zhengzhou University, Zhengzhou 450001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"119218","DOI":"10.1016\/j.oceaneng.2024.119218","article-title":"Ship trajectory segmentation by movement states while addressing uncertainty and sparsity","volume":"312","author":"Guo","year":"2024","journal-title":"Ocean Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"109256","DOI":"10.1016\/j.oceaneng.2021.109256","article-title":"Improved kinematic interpolation for AIS trajectory reconstruction","volume":"234","author":"Guo","year":"2021","journal-title":"Ocean Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"755","DOI":"10.1080\/01441647.2019.1649315","article-title":"How big data enriches maritime research\u2013a critical review of Automatic Identification System (AIS) data applications","volume":"39","author":"Yang","year":"2019","journal-title":"Transp. 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