## 1 Introduction  
Remote sensing imagery (RSI) plays a vital role in a wide range of applications, such as land cover, land use, and urban management applications [1, 2]. In the past, the semantic segmentation of RSI was mainly carried out with manual methods or traditional machine learning approaches. Manual methods require human analysts to visually interpret the remote sensing imagery and manually delineate the different objects of interest, which is time-consuming, labor-intensive, and subject to human subjectivity and errors. However, human interpretation is often considered reliable due to the expertise and domain knowledge of the analysts. Traditional machine learning approaches, such as supervised classification algorithms, have also been employed for semantic segmentation of RSI. These approaches involve training a classifier on manually labeled training samples, and then the trained classifier predicts labels for unseen testing samples. Decision trees, support vector machines (SVMs), and random forests are commonly used algorithms. In recent years, deep learning methods have been successfully used in various fields, such as for facial age estimation [3], super-resolution satellite images [4], clothing retrieval [5], and object identification in satellite images [6, 7]. Deep learning has also become a powerful approach for segmentation of RSI [8]. Deep learning-based models, such as convolutional neural networks (CNNs), can learn hierarchical representations directly from the raw images, enabling them to capture complex patterns and spatial dependencies in remote sensing imagery. These models have significantly advanced the accuracy and efficiency of semantic segmentation in RSI. They also have demonstrated superior performance by combining channel, spatial, and contextual information, making them highly effective for extracting detailed and accurate semantic information from remote sensing images. Semantic segmentation on remote sensing images (RSI) is more challenge than on natural images. The encoder-decoder architectures of SegNet [9] and U-Net [10] are considered the most common semantic segmentation models and mainly fuse feature maps of different scales to obtain more diverse features. In past studies, it can be found that the use of U-Net architecture-based models for semantic segmentation on RSI can help the models to perform segmentation tasks more effectively, but U-Net does not make special adjustments in the connection process, which can also result in the model not fully utilizing the extracted features. Recently, the adaptive focus framework (AF2) [11] was employed with a hierarchical neural network to perform aerial imagery segmentation. The foreground-aware relation network (FarSeg++) [12] has been proposed as a multi-perspective model to alleviate the larger intra-class variance in backgrounds and the foreground-background imbalance in high-spatial-resolution (HSR) remote sensing imagery. The global attention-based fusion network (GAF-Net) [13] adopts a novel fusion architecture to improve performance with remote sensing images. In response to the aforementioned problems, we propose a new model called MSCSA-Net, which employs two newly proposed modules for effective prediction tasks when it is used in the training stage. The primary contributions of our proposed MSCSA-Net are summarized below. * More discriminative features: We developed a local channel spatial attention (LCSA) module to extract local feature maps to assist our model in the prediction of the scenes;
* Better recognition and lower computation complexity: We developed a multi-scale attention (MSA) module, which is modified from ASPP [14], to perform separable convolution with different sizes of feature maps with a much lower number of parameters.