{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T09:52:36Z","timestamp":1767693156867,"version":"3.48.0"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T00:00:00Z","timestamp":1767657600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2021YFF0500902"],"award-info":[{"award-number":["2021YFF0500902"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Establishing digital twin scenes facilitates the understanding of geospatial phenomena, representing a significant research focus for GIS scientists and engineers. However, current research on digital twin scenes modeling relies on manual intervention or the overlay of static models, resulting in low modeling efficiency and poor standardization. To address these challenges, this paper proposes a knowledge graph-guided and multimodal data fusion-driven rapid modeling method for digital twin scenes, using bridge tower construction as an illustrative example. We first constructed a knowledge graph linking the three domains of \u201cevent-object-data\u201d in bridge tower construction. Guided by this graph, we designed a knowledge graph-guided multimodal data association and fusion algorithm. Then a rapid modeling method for bridge tower construction scenes based on dynamic data was established. Finally, a prototype system was developed, and a case study area was selected for analysis. Experimental results show that the knowledge graph we built clearly captures all elements and their relationships in bridge tower construction scenes. Our method enables precise fusion of 5 types of multimodal data: BIM, DEM, images, videos, and point clouds. It improves spatial registration accuracy by 21.83%, increases temporal fusion efficiency by 65.6%, and reduces feature fusion error rates by 70.9%. Local updates of the 3D geographic scene take less than 30 ms, supporting millisecond-level digital twin modeling. This provides a practical reference for building geographic digital twin scenes.<\/jats:p>","DOI":"10.3390\/ijgi15010027","type":"journal-article","created":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T09:40:29Z","timestamp":1767692429000},"page":"27","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Knowledge Graph-Guided and Multimodal Data Fusion-Driven Rapid Modeling Method for Digital Twin Scenes: A Case Study of Bridge Tower Construction"],"prefix":"10.3390","volume":"15","author":[{"given":"Yongtao","family":"Zhang","sequence":"first","affiliation":[{"name":"CCCC Second Harbor Engineering Company Ltd., Wuhan 430048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8763-6674","authenticated-orcid":false,"given":"Yongwei","family":"Wang","sequence":"additional","affiliation":[{"name":"CCCC Second Harbor Engineering Company Ltd., Wuhan 430048, China"}]},{"given":"Zhihao","family":"Guo","sequence":"additional","affiliation":[{"name":"Faculty of Geosiences and Engineering, Southwest Jiaotong University, Chengdu 611759, China"}]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Geosiences and Engineering, Southwest Jiaotong University, Chengdu 611759, China"},{"name":"State Key Laboratory of Bridge Intelligent and Green Construction, Chengdu 611759, China"}]},{"given":"Fanxu","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Geosiences and Engineering, Southwest Jiaotong University, Chengdu 611759, China"}]},{"given":"Hao","family":"Zhu","sequence":"additional","affiliation":[{"name":"CCCC Second Harbor Engineering Company Ltd., Wuhan 430048, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0107-4034","authenticated-orcid":false,"given":"Yuan","family":"Chen","sequence":"additional","affiliation":[{"name":"CCCC Second Harbor Engineering Company Ltd., Wuhan 430048, China"}]},{"given":"Yajian","family":"Kang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Intelligent Parallel Technology, Beijing 100072, China"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1080\/13658816.2023.2298299","article-title":"A knowledge-guided visualization framework of disaster scenes for helping the public cognize risk information","volume":"38","author":"Zhu","year":"2024","journal-title":"Int. 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