{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T00:13:52Z","timestamp":1766708032300,"version":"3.48.0"},"reference-count":37,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T00:00:00Z","timestamp":1766620800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program","doi-asserted-by":"publisher","award":["2021YFF0704400"],"award-info":[{"award-number":["2021YFF0704400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41971351"],"award-info":[{"award-number":["41971351"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42001372"],"award-info":[{"award-number":["42001372"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Accurately classified surface water datasets are critical for hydrological modeling, environmental monitoring, and water resource management. Most large-scale datasets are raster-based, produced through pixel-level classification. Existing global vector datasets often struggle to capture small water bodies and maintain global consistency. Therefore, extracting vector features from Earth observation raster products and performing fine-grained classification is a promising approach, but fragmentation and the lack of object-level semantic labels remain key challenges. This study, based on the JRC Global Surface Water dataset, proposes a low-fragmentation global-scale vector dataset for river and lake classification. Our workflow integrates a fragment-aggregating strategy with a water body classification model. Specifically, we implemented a three-stage aggregation process using GIS-based hydrological constraints, classification buffering, and neighbor analysis to reduce fragmentation. A deep learning classifier combining convolutional feature extraction with Transformer-based contextual reasoning performs contour-informed classification of water bodies. Experiments show that the aggregation strategy reduces water body fragmentation by nearly 60%, while the classifier achieves an F1 score of 92.4%. These results demonstrate that our approach provides a transferable solution for constructing surface water classification datasets, delivering valuable resources for remote sensing, ecology, and hydrological decision-making.<\/jats:p>","DOI":"10.3390\/ijgi15010012","type":"journal-article","created":{"date-parts":[[2025,12,25]],"date-time":"2025-12-25T23:52:27Z","timestamp":1766706747000},"page":"12","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Low-Fragmentation Global Vector Dataset for River and Lake Classification of Surface Water Bodies"],"prefix":"10.3390","volume":"15","author":[{"given":"Dinan","family":"Wang","sequence":"first","affiliation":[{"name":"National Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengxiang","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"School of Artificial Intelligence and Information, Jiangxi Institute of Construction, Nanchang 330200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zeqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Geographic Information System, China University of Geosciences (Wuhan), Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weibo","family":"Su","sequence":"additional","affiliation":[{"name":"College for Elite Engineers, China University of Geosciences (Wuhan), Wuhan 430074, China"},{"name":"Wuhan Gaea Space-time Information Technology Co., Ltd., Wuhan 430223, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2018.08.014","article-title":"An automated mathematical morphology driven algorithm for water body extraction from remotely sensed images","volume":"146","author":"Rishikeshan","year":"2018","journal-title":"ISPRS J. 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