Accurate river extraction is crucial for agricultural irrigation, water conservancy planning, and flood warning. To mitigate the issues of excessive detail loss and scarcity of labeled data in existing encoder-decoder networks, we propose a non-uniform sampling method combined with graph-based semi-supervised learning to leverage unlabeled data effectively. The method samples more points in high-frequency regions (e.g., river edges) and fewer in low-frequency regions, followed by bilinear interpolation for feature fusion. Experimental results on the Gaofen-2 dataset demonstrate that our method improves Unet, Linknet, and DeeplabV3 by 0.9, 1.5, and 1.6% in accuracy, and by 1.7, 2.9, and 1.9% in IoU, respectively. With semi-supervised learning, using all unlabeled data boosts pixel accuracy by 5.0% and IoU by 9.3%. Additionally, evaluations on the OpenEarthMap dataset and comparisons with state-of-the-art SSL methods further confirm the robustness and generalization capability of our framework.
Wang et al. (2026) studied this question.