## 1 Introduction
This module has the function of aggregating the contextual information from diverse regions. Through such aggregation, it effectively boosts the capacity of the network to acquire global information. DeepLabV3+ balances the exactness in segmentation with the model training time by the null convolution, which has the capacity to extract the output features from the network and use them to work out images with distinct resolutions. It is able to adjust the perceptual field with constant feature map size to extract multi-scale information. The U-Net network model, which is an improved fully convolutional neural network, inherits the features of fully convolutional neural networks, allowing the model to utilize the feature connectionsto the fullest between the higher and lower layers to obtain accurate output results. Among various deep learning models, the U-Net network model has stable performance, strong learning power, and high robustness. It was initially used for detecting medical images and later widely used in building extraction, while for complex remote sensing image information extraction, detailed information would be lost in maximum pooling sampling, so related scholars proposed improved algorithms. [PERSON] et al. [27] put forward a fully convolutional U-network model to establish close connections to pull out contextual information fully and completely, which boosts the exactness in building extraction by improving the model structure and post-processing but increases the complexity of building extraction. [PERSON] et al. [28] introduced a U-Net (DHAU-Net) based on a dual hybrid attention mechanism, which enhances feature integration and uses the attention mechanism to focus on relevant image details. [PERSON] et al. [29] proposed a refined U-Net network (Refined-UNET) with the aim of pulling out buildings more accurately by introducing an expanded spatial convolution pyramid pooling (ASPP) module and an improved depth detachable convolution (IDSC) module. [PERSON] et al. [30] incorporated a spatial attention mechanism and a channel attention mechanism within the U-Net network. The aim of this incorporation was to mitigate the information disparities among features at various hierarchical levels. The U-Net-based multi-loss neural network model proposed by [PERSON] et al. [31] added the attention module to improve model sensitivity and curb the effect of unrelated feature regions. To solve the problem of low computational efficiency, [PERSON] et al. [32] proposed an asymmetric convolutional residual block network to reduce the model's size. Even though these approaches possess certain benefits for extracting buildings, the following drawbacks remain: (1) For the scale invariance and structural complexity of buildings within high-resolution remote sensing images, the ability to extract small-scale or irregular buildings is insufficient, and there are problems of wrong detection, missed detection and unclear building contours. (2) The model's expression of global features is not enough, and voids appear within buildings. (3) The inter-class variability of buildings is low. To address the abovementioned problems, this paper puts forward F EPA-Net. A high-resolution remote sensing image building extraction network is what it is, which integrates feature extraction and position attention modules. This network is established on the architectural framework of the U-Net model. The model employs parallel dilated convolution with different receptive field in the input feature mapping by increasing the times of convolutions and fusing them together to reduce the computation, expanding the receptive field, and avoiding the convolutional degradation caused by convolution with a too large expansion rate while applying global averaging on the final feature mapping of the feature extraction module, convolving the result by 1 \(\times\) 1, and then bilinear up-sampling to obtain the required spatial dimension.