## 7 Conclusions  
This study presents the UniU-Net model, which demonstrates significant potential for the high-precision segmentation of area palms in remote sensing imagery. By incorporating an auxiliary encoder and a unified attention fusion module (UAFM), the model provides a novel solution to the challenges of accurately extracting tree boundaries and spatial relationships, particularly in complex environments. The experimental results show that UniU-Net achieves superior performance over existing models, marking a substantial advancement in automated tree extraction techniques. The contributions of this research extend beyond the immediate task of area palm segmentation. The model's innovative use of auxiliary encoding and attention mechanisms offers valuable insights that could enhance the segmentation accuracy in other remote sensing applications. As remote sensing datasets continue to expand, UniU-Net has the potential to be applied to a broader range of tree species and environmental settings, providing a foundation for future advancements in automated tree extraction and land cover classification.  
In future work, three key areas offer the potential to further enhance the model's capabilities and applications. Firstly, expanding the dataset is considered essential to improve the model's generalization ability. By including a greater number of diverse geographic regions, tree species, and environmental conditions, the model's robustnessand applicability across different scenarios will be strengthened. Secondly, the fusion of multi-modal remote sensing data, particularly the combination of optical imagery with SAR data, will provide richer spatial and structural information, thus enhancing the model's performance and accuracy. Finally, the incorporation of more advanced models and techniques, such as transfer learning and refined attention mechanisms, will improve the efficiency and scope of the model's applications, enabling it to tackle more complex remote sensing tasks.  
In conclusion, this study offers a novel and effective approach for area palm segmentation in remote sensing imagery. The findings provide a valuable contribution to the field of remote sensing and automated tree extraction. Moving forward, with continued research and refinement, UniU-Net has the potential to significantly improve the accuracy, efficiency, and applicability of remote sensing technologies across a variety of environmental and agricultural monitoring tasks.  
Conceptualization, S.W. and J.Y.; methodology, Y.W.; software, Y.W., Z.Z. and B.L.; validation, Y.W., Z.Z. and B.L.; resources, S.W. and H.L.; data curation, Y.W.; writing--original draft preparation, Z.Z., B.L. and Y.W.; writing--review and editing, H.L.; visualization, H.L. and Y.W.; supervision, S.W. and J.Y.; project administration, S.W., H.L. and J.Y.; funding acquisition, S.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Natural Science Foundation of Hainan Province of China (Grant 323 MS110), China; the talent introduction program Youth Project of the Chinese Academy of Sciences (E43302020D, EZ210501), China; and the innovation group project of the Key Laboratory of Remote Sensing and Digital Earth of the Chinese Academy of Sciences (E33D0201-5), China.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this study.