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Ames, Daniel P (Ed.)Hydrological streamline delineation is critical for effective environmental management, influencing agriculture sustainability, river dynamics, watershed planning, and more. This study develops a novel approach to combining transfer learning with convolutional neural networks that capitalize on image-based pre-trained models to improve the accuracy and transferability of streamline delineation. We evaluate the performance of eleven image-based pre-trained models and a baseline model using datasets from Rowan County, North Carolina, and Covington River, Virginia in the USA. Our results demonstrate that when models are adapted to a new area, the fine-tuned ImageNet pre-trained model exhibits superior predictive accuracy, markedly higher than the models trained from scratch or those only fine-tuned on the same area. Moreover, the pre-trained model achieves better smoothness and connectivity between classified streamline channels. These findings underline the effectiveness of transfer learning in enhancing the delineation of hydrological streamlines across varied geographies, offering a scalable solution for accurate and efficient environmental modelling.more » « lessFree, publicly-accessible full text available October 1, 2025
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Xu, Zewei ; Wang, Shaowen ; Stanislawski, Lawrence V. ; Jiang, Zhe ; Jaroenchai, Nattapon ; Sainju, Arpan Man ; Shavers, Ethan ; Usery, E. Lynn ; Chen, Li ; Li, Zhiyu ; et al ( , Environmental Modelling & Software)null (Ed.)