{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T17:22:49Z","timestamp":1771521769916,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,6,21]],"date-time":"2023-06-21T00:00:00Z","timestamp":1687305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Fund of China","doi-asserted-by":"publisher","award":["42071455"],"award-info":[{"award-number":["42071455"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Drainage network pattern recognition is a significant task with wide applications in geographic information mining, map cartography, water resources management, and urban planning. Accurate identification of spatial patterns in river networks can help us understand geographic phenomena, optimize map cartographic quality, assess water resource potential, and provide a scientific basis for urban development planning. However, river network pattern recognition still faces challenges due to the complexity and diversity of river networks. To address this issue, this study proposes a river network pattern recognition method based on graph convolutional networks (GCNs), aiming to achieve accurate classification of different river network patterns. We utilize binary trees to construct a hierarchical tree structure based on river reaches and progressively determine the tree hierarchy by identifying the upstream and downstream relationships among river reaches. Based on this representation, input features for the graph convolutional model are extracted from both spatial and geometric perspectives. The effectiveness of the proposed method is validated through classification experiments on four types of vector river network data (dendritic, fan-shaped, trellis, and fan-shaped). The experimental results demonstrate that the proposed method can effectively classify vector river networks, providing strong support for research and applications in related fields.<\/jats:p>","DOI":"10.3390\/ijgi12070253","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T02:09:17Z","timestamp":1687399757000},"page":"253","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Drainage Pattern Recognition of River Network Based on Graph Convolutional Neural Network"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4738-8095","authenticated-orcid":false,"given":"Xiaofeng","family":"Xu","sequence":"first","affiliation":[{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"},{"name":"Wuhan Natural Resources and Planning Information Center, Wuhan 430014, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0874-1538","authenticated-orcid":false,"given":"Pengcheng","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China"},{"name":"Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingwu","family":"Guo","sequence":"additional","affiliation":[{"name":"Wuhan Geomatics Institute, Wuhan 430022, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,21]]},"reference":[{"key":"ref_1","first-page":"154","article-title":"A Multi-criteria Decision Model for Identifying Master River and Its Application in River System Construction","volume":"34","author":"Tan","year":"2005","journal-title":"Acta Geod. 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