<p>In this paper, we propose SegRSNet for the task of feature extraction from remote sensing images. To address the challenges of complexity and variability of target objects, obvious occlusion effects and rich multilevel semantic information in high-resolution remote sensing images, we design a plug-and-play network structure, SegRS Block, which consists of a series of key components responsible for: efficiently aggregating channel features, accurately capturing spatial location information, fusing feature maps across layers and deepening the fine modeling of channel dimensions. The experimental results show that SegRSNet achieves state-of-the-art (SOTA) performance on multiple benchmark datasets for both building and road feature extraction, which not only outperforms the existing best methods under the same parameter scale, but also shows high adaptability and accuracy for all kinds of remote sensing image analysis tasks. In addition, although the Transformer architecture has advantages in dealing with remote dependencies, it usually requires large-scale training data and a large number of parameters. In contrast, our study shows that the combination of convolutional neural network and a specially designed attention module can reduce the training cost while improving the receptive field to effectively deal with the remote sensing image segmentation problem, thus realizing the detailed and accurate recognition and parsing of multi-scenario remote sensing data.</p>