## 5 Conclusions  
This study proposed LKF-DCANet, an efficient water body segmentation framework that integrates a learnable Kalman filter, lightweight feature extraction, self-attention mechanisms, and feature distillation. The framework balances representation capacity with computational efficiency and demonstrates strong adaptability to fragmented water bodies and complex boundary structures. Experimental results confirm its high accuracy and low computational cost on both public remote sensing datasets and UAV imagery.  
Future work will focus on enhancing the model's robustness under extreme weather conditions, discontinuous water structures, and complex background interference. Inaddition, we will explore the integration of multi-modal remote sensing data and domain-adaptive training strategies to improve generalization in unseen agricultural regions. To further enhance deployment feasibility in large-scale satellite scenes, we also plan to incorporate hierarchical resolution processing, memory-efficient tile streaming, and patch aggregation mechanisms. These improvements aim to optimize the model's parallelism and reduce latency, supporting real-time and low-power agricultural applications.  
Conceptualization, D.L. and J.S.; methodology, J.S.; software, D.L.; validation, Z.D., Y.Z. and D.O.; formal analysis, J.S.; investigation, D.L.; resources, J.S.; data curation, Z.D., Y.Z. and J.Z.; writing--original draft preparation, D.L.; writing--review and editing, J.S.; visualization, Z.D.; supervision, D.O.; project administration, D.O.; funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.  
This work is supported by Open Fund of Key Laboratory of Investigation, Monitoring, Protection and Utilization for Cultivated Land Resources, Ministry of Natural Resources (Grant Nos. CLRKI2024 GP10; CLRKL2024 KP02), and Observation and Research Station of Land Ecology and Land Use in Chengdu Plain, Ministry of Natural Resources, P.R. China (Grant Nos. CDORS-2024-06; CDORS-2024-08), and the Natural Science Foundation of Sichuan, China (No. 2024 NSFSC0075); Provincial Undergraduate Training Program on Innovation and Entrepreneurship (No. S202410626024).  
Not applicable.  
The data and algorithm code can be obtained at [[https://github.com/Estrellading/LKF-DCANet](https://github.com/Estrellading/LKF-DCANet)]([https://github.com/Estrellading/LKF-DCANet](https://github.com/Estrellading/LKF-DCANet)) (accessed on 27 May 2025).  
The authors sincerely thank the editor and anonymous reviewers for their constructive comments and suggestions, which have greatly improved the quality of this manuscript. The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
LKF-DCANet Learnable Kalman Filter and Deformable Convolutional Attention Network  
LKF Learnable Kalman Filter  
CADCN Channel Attention-Enhanced Deformable Convolutional Network  
CATM Convolutional Additive Token Mixer  
GELU Gaussian Error Linear Unit  
FLOPs Floating Point Operations  
Params Parameters  
IoU Intersection over Union  
F1 F1-Score  
UAV Unmanned Aerial Vehicle  
CNN Convolutional Neural Network  
DJI Da Jiang Innovations  
GPU Graphics Processing Unit  
CUDA Compute Unified Device Architecture