A Low-Fragmentation Global Vector Dataset for River and Lake Classification of Surface Water Bodies

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.