## 2 Related Works  
### Land Cover Extraction by Deep Learning  
For a long time, technicians have been implementing fully manual or semi-automated remote sensing extraction of land cover in China through human-computer interaction. Manual extraction of geographic elements from high-resolution remote sensing images is inefficient, time-consuming, costly, and unable to meet processing requirements in a timely manner, so semi-automatic and fully automatic methods have been preferred. Currently, land cover classification has evolved from pixel image analysis [2; 20] to object-based image analysis [21]. Compared with pixel image analysis, object-based image analysis makes full use of various features of objects, such as spectral features, texture features, and geometric features. Recently, deep learning has been pro-posed to automatically learn abstract features and has proven to be a state-of-the-art approach. It can provide an end-to-end framework from raw data to target results, reduce human-computer interaction, and greatly improve the efficiency and automation of remote sensing classification [22]. In particular, convolutional neural networks (CNNs) have dominated many areas of computer vision, achieving good results in object recognition, detection, and segmentation. To some extent, these research results can be directly used for remote sensing classification and feature extraction. These applications usually use RGB images, while the current multispectral images acquired by satellite remote sensing, which have a lower cost, the widest coverage, higher resolution, and more application fields, also contain RGB bands. Given the practical application requirements, this study also adopts RGB image data as the basic data. The birth of technologies such as CNN, attention mechanism, and Transformer have accelerated the research on practical intelligent classification and extraction methods for land cover [23]. CNN is one of the representative models for deep learning applications on remote sensing data [24; 25] aiming to utilize the multidimensional grid structure of the input image [26]. Through weight sharing and local connectivity, CNNs build deep networks that can learn features from the pixel level to the semantic level for remote sensing applications. The features extracted by CNN are fed into classifiers such as SVM and random forest in order to obtain better classification results. [PERSON] proposed a deep patch-based CNN framework for medium-resolution remote sensing data, which targets 5 \(\times\) 5 patches and improves the classification accuracy compared to pixel-based classification methods [27]. [PERSON] and [PERSON] used deep CNN with a fully connected U-Net backbone to extract field boundaries from remote sensing images [28]. CNNs rely on the assumption of independence between instances, which is unacceptable for temporally or spatially correlated data [29]. Local receptive fields in CNNs limit the modeling of long-distance dependencies in images, while the convolution is content-independent. Since the convolutional filter weights are fixed, their values are the same regardless of the attributes of the input. More recently, Transformer-based architectures, originally introduced in natural language processing, have penetrated the field of computer vision, in which the self-attention mechanism is used as an alternative to the popular convolutional operator to capture long-distance dependencies. Due to its attention mechanism, Vision Transformer (ViT) can efficiently capture global interactions by learning the relationships between sequence elements [30]. After the success of ViT in the field of computer vision, the remote sensing community has also introduced it into research on ultra-high-resolution image classification [31], change detection, etc. A two-stream Swin Transformer network (TSTNet) is proposed to address the challenges of complex backgrounds and various stochastically arranged objects on remote sensing images, which makes it difficult to focus on the target objects in the scene [32].