# Segmenting Urban Scene Imagery in Real Time Using an Efficient UNet-like Transformer
## 1 Introduction  
In recent years, with the rapid development of satellite and sensor technologies, a large number of remote sensing urban scene images can be easily obtained for a variety of different applications, such as semantic segmentation [1, 2], image classification [3, 4], and target detection [5]. As a classic computer field method, semantic segmentation aims to take image pixels as the basic segmentation unit to accurately classify the category attributes of each pixel on the image, which leads to more in-depth applications, such as road and building extraction [6, 7], land use and land cover (LULC) [8, 9], and change detection [10, 11]. For example, we can quickly and accurately obtain the basic information of different buildings in the city through automatic building extraction, contributing to the smart cities' construction. Extracting urban green space is conducive to monitoring urban greening and contributing to a more comfortable urban environment. Recently, the deep learning technology has rapidly developed. As a representative branch, CNN has been the mainstream in the segmentation field for many years. CNN-based methods are better at capturing rich local spatial relationships compared with traditional machine learning methods, such as random forests (RF) [12] and support vector machines (SVM) [13]. In addition, the powerful feature representation capability has made them quickly surpass the traditional method to become a mainstream segmentation method in many fields. Despite the above advantages, the CNN-based methods use the convolution kernel with a fixed receptive field to extract local information, which limits its potential to model long-range dependencies or global spatial information. For the tasks of semantic segmentation, the lack of global contextual information can produce ambiguous and low-accuracy segmentation results. To cope with this problem, some studies [14; 15] utilized the attention mechanism to improve the model's potential to model global context. Although the attention mechanism alleviates the above problems to some extent, it still cannot get rid of the local nature of convolution operations and incurs additional computational costs, thus limiting the model's efficiency in different tasks and the potential in real-time applications. Recently, the emergence of Transformer [16] has provided a new way to solve the above problems. The natural language processing (NLP) tasks first used Transformer to process sequential data, and its great success inspired scholars to explore its application potential in other fields, such as multimodal fusion and computer vision (CV). By eliminating convolution operations and relying solely on efficient multi-layer perceptron (MLP) and multi-head self-attention mechanisms, the Transformer is better at capturing the global context. In the semantic segmentation and image classification tasks, there have been many studies demonstrating that models based on the Transformer structure have strong global information modeling capabilities, which can significantly improve the final classification or segmentation results. [PERSON] et al. [3] combined two branches (CNN and ViT) to design an efficient multi-branch Transformer for imagery classification in remote sensing, which reduced the model complexity and improved the long-range dependency modeling capability. [PERSON] et al. [17] proposed a U-shaped novel network structure based on the CNN encoder and Transformer decoder for high-resolution urban scene imagery semantic segmentation, and the powerful global information modeling module in the decoder enables it to obtain good results on challenging datasets. [PERSON] et al. [18] introduced the Swin Transformer as the main encoder to extract contextual semantic information; meanwhile, a densely connected feature aggregation module (DCFAM) was designed in the decoder to improve the final accuracy. [PERSON] et al.