Drainage network extraction is essential for different research and applications. However, traditional methods have low efficiency, low accuracy for flat regions, and difficulties in detecting channel heads. Although deep learning techniques have been used to solve these problems, different challenges remain unsolved. Therefore, we introduced distributed representations of aspect features to facilitate the deep learning model calculating the flow direction; adopted a semantic segmentation model, U-Net, to improve the accuracy and efficiency in predicting flow directions and in pixel classifications; and used postprocessing to delineate the flowlines. Our proposed framework achieved state-of-the-art results compared with the traditional methods and the published deep-learning-based methods. Further, case study results demonstrated that our framework can extract drainage networks with high accuracy for rivers of different widths flowing through terrains of different characteristics. This framework, requiring no parameters provided by users, can also produce waterbody polygons and allow cyclic graphs in the drainage network.

排水网络提取对于不同的研究和应用至关重要。然而，传统方法效率低，平坦区域精度低，通道头检测困难。尽管深度学习技术已被用于解决这些问题，但仍有不同的挑战尚未解决。因此，我们引入了方面特征的分布式表示，以方便深度学习模型计算流向；采用语义分割模型U-Net，提高预测流向和像素分类的准确性和效率；并使用后处理来描绘流线。与传统方法和已发布的基于深度学习的方法相比，我们提出的框架取得了最先进的结果。更多，案例研究结果表明，我们的框架可以为流经不同特征地形的不同宽度的河流高精度提取排水网络。该框架不需要用户提供参数，也可以生成水体多边形并允许在排水管网中生成循环图。