## 1 Introduction
It first splits and serializes the entire image, introduces the image positional information by adding position embeddings, and then uses the Transformer structure to process the resulting sequence of vectors to obtain more powerful image features, thereby improving the accuracy of image semantic segmentation. Since ViT splits the entire images into fixed-size patches, the result is rough when the patch size is large, while the memory consumption and computation amount are large when the patch size is small. To this end, some researchers have proposed a Swin Transformer [27] with a hierarchical design through shifted windows, which limits the self-attention computation to local windows through a sliding window operation, thereby significantly reducing computational complexity while retaining the details of the image. Semantic segmentation networks utilizing Transformers necessitate substantial training data and computational resources. Despite numerous scholarly efforts to enhance and optimize these networks, exemplified by innovations like Swin Transformer [27], TopFormer [28] and SiamixFormer [29], they can only mitigate training complexity to a certain degree. To this end, some researchers have designed semantic segmentation networks that combine CNNs and Transformers, such as TransUNet. TransUNet [30] is one of the most classic hybrid networks, which improves the classic U-Net network by adding a series of Transformer units at the end of the encoder to globally model deep features with global context information, thereby significantly improving the accuracy of image segmentation. Since then, [PERSON] et al. [31] introduced the CNN and Multiscale Transformer Fusion Network (CMTFNet), an encoder-decoder architecture that integrates CNN and transformer techniques to extract local details and merge multiscale global context for precise high resolution remote-sensing image segmentation. With transformer as the backbone network, STransFuse [32], Cmt [33] and CMFNet [34] have emerged. STransFuse [32] incorporates swim transformer coding branches alongside CNN coding, extracting feature representations at various scales through hierarchical stages. Cmt [33] is a transformer-based hybrid network that substitutes convolution for the multilayer perceptron in the transformer, enhancing model accuracy without sacrificing speed. CMFNet [34] is a crossmodal multiscale fusion network that utilizes transformer architecture to capture long-range dependencies across multiscale convolutional feature maps of remote sensing data from diverse modalities, with the goal of enhancing semantic segmentation. Deep learning-based semantic segmentation has achieved good results in the field of natural images and remote sensing images. However, classical semantic segmentation algorithms are mainly focus on processing small-sized images. Compared with natural images, remote sensing images have high resolution and large format, and there are many large-sized objects with weak-texture areas such as large building roofs, waters, and vegetation. Small-sized images (e.g., 256 \(\times\) 256) in these areas contain less information and often have low segmentation accuracy in these areas. Meanwhile, due to the limitations of graphics processing unit (GPU) memory, large-format high-resolution images (e.g., 2048 \(\times\) 2048) cannot be processed directly. Typical solutions such as downsampling, patch cropping, and the use of cascade models often entail a trade-off where they sacrifice intricate details or comprehensive contextual information, leading to accuracy constraints. To address this issue, we propose a novel global-local feature aggregation framework for the semantic segmentation of large-format high-resolution remote sensing images. This framework extracts global information and local details through two branches, which are then aggregated to effectively resolve category determination in weakly textured areas with fewer spatial features, such as water bodies and large building rooftops.