## 6 Conclusions  
This research presents an end-to-end deep learning framework designed for the nuanced extraction of water bodies, employing a synergistic approach through the amalgamation of Transformer and CNN methodologies. This innovative model is meticulously configured to concurrently assimilate fine-grained local and comprehensive global information present in feature maps. One of the pivotal enhancements implemented within this framework is the refinement of CBAM in DCB-ConvNet segment. This is further complemented by the incorporation of deformable convolution techniques, significantly sharpening the system's acuity in identifying water body perimeters and augmenting its capability in detailed local information capture. Parallelly, the Transformer segment exploits its inherent self-attention mechanism, offering a solution to the inherent constraints of traditional convolution procedures, which exhibit deficiencies in managing extensive spatial dependencies. This strategic enhancement fortifies the model's capacity for comprehensive feature learning, thereby boosting the uniformity and reliability of outcomes in the context of water bodies extractions. Addressing the potential contamination of high-level features with irrelevant background constituents, the model integrates a FPA module. This critical inclusion serves to meticulously filter out non-water bodies elements, intensifying the focus on multi-scale water-related information, and substantially improving the accuracy of detecting smaller-scale water bodies. The model's accuracy and adaptability, as demonstrated by 93.7% F1 score on the challenging Yellow River Basin dataset, are further validated through rigorous ablation testing and comparative assessments, establishing its suitability for complex, large-scale applications. Its practical efficacy is additionally evidenced by successful, extensive mapping exercises conducted within the Yellow River Basin. Future initiatives aim to expand the utility of this cutting-edge model by undertaking comprehensive assessments of temporal fluctuations in the Yellow River Basin's water bodies over a triennial period, with a concentrated focus on discerning patterns within both seasonal and permanent water bodies. Concurrently, there is a commitment to enhancing the operational efficiency of the network, striving for a leaner configuration that retains, or potentially elevates, its analytical prowess.  
Conceptualization, M.G.; methodology, M.G. and X.Z.; validation, M.G. and X.Y.; formal analysis, C.L.; resources, C.L.; data curation, X.Z.; writing--original draft preparation, M.G.; writing--review and editing, X.Z. and X.Y.; visualization, M.G.; supervision, X.Z.; project administration, C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Key Research and Development Program of China (No. 2021 YFB3901300).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to currently proprietary.  
The authors are grateful to the editors and anonymous reviewers for their informative suggestions.  
The authors declare no conflict of interest.  
The following abbreviations are used throughout this manuscript:  
\begin{tabular}{l l} ASPP & \multicolumn{1}{c}{atrous spatial pyramid pooling} \\ BCE & \multicolumn{1}{c}{binary cross entropy} \\ CBAM & \multicolumn{1}{c}{convolutional block attention module} \\ CNN & \multicolumn{1}{c}{convolutional neural network} \\ \end{tabular}