## 5 Conclusions  
By using the FEPA-Net model to precisely extract buildings from high-resolution remote sensing images, the following conclusions were drawn:  
(1) The FEPA-Net model increases the number of convolutional operations in the main part of feature extraction. It utilizes dilated convolutions with varying dilation rates on the input feature maps and integrates a module for extracting features, with the aim of obtaining the global and comprehensive information of high-resolution images. This expands the receptive field and prevents the occurrence of holes when extracting buildings.  
(2) The FEPA-Net model incorporates the position attention module to obtain more abundant contextual information, thereby reducing the loss of building detail features, which not only boosts the network's generalization ability but also enhances the completeness of building extraction.  
(3) To validate the effectiveness of the FEPA-Net model, optimization and training are performed based on the Massachusetts dataset and WHU aerial image dataset, respectively, and automated building extraction is performed with PSPNet, DeepLabv3+, U-Net, and other networks for remote sensing images, and the results of qualitative and quantitative analyses show that the FEPA-Net model achieved improved results compared with the other methods.  
Although the method presented in this paper can extract building information effectively, its prediction accuracy has the potential to improve. Regarding the model structure, it is feasible to contemplate incorporating other algorithms to enhance the applicability of the building extraction method, and the extraction performance merits further exploration.  
[PERSON], [PERSON] and [PERSON] conceived the project, formulated the key conceptual notions, and produced the proof framework. [PERSON] carried out the neural network training and wrote the initial draft. [PERSON] and [PERSON] conducted an analysis of the background literature. [PERSON], [PERSON] and [PERSON] took charge of timely communication and coordination with all co-authors. [PERSON], [PERSON] and [PERSON] made critical revisions to the article. [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] participated in the preparation of figure items at different stages. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Education Bureau of Liaoning Province, China (IJKMZ20220638), and the National Natural Science Foundation of China (42471371).  
Not applicable.  
Data Availability Statement: The original data presented in the study are openly available at [[[https://www.cs.toronto.edu/~vmnih/data](https://www.cs.toronto.edu/~vmnih/data)]([https://www.cs.toronto.edu/~vmnih/data](https://www.cs.toronto.edu/~vmnih/data)) (accessed on 15 October 2023)], [[[https://gpcv.whu.edu.cn/data/building_dataset.html](https://gpcv.whu.edu.cn/data/building_dataset.html)]([https://gpcv.whu.edu.cn/data/building_dataset.html](https://gpcv.whu.edu.cn/data/building_dataset.html)) (accessed on 15 October 2023)].  
We deeply appreciate all those who lent us a hand during the research and writing process of this paper. Moreover, we sincerely express our gratitude to everyone who dedicated a significant amount of time to reading this thesis and provided us with numerous valuable suggestions that will be beneficial for our future studies.  
The authors declare no conflicts of interest.