## 1 Introduction
[12] constructed a neural network (TR-CNN) that fused time-series power data and remote sensing images to identify urban land use types. [PERSON] et al. [43] combined U-Net and DenseASPP (Densely connected Atrous Spatial Pyramid Pooling) convolutional neural network utilized the attention mechanism to efficiently fuse multi-source semantic information from the output of the two-layer network, and realized high-precision urban land use classification. Refining land use classification from remote sensing images and inputting them into deep learning models enables these models to effectively analyze and reason about land use classification. This approach allows for quick and accurate identification of construction categories within the existing built environment, ultimately leading to more precise and efficient evaluation processes. Table 1 provides a concise summary of the literature references for urban land use classification using deep learning models. These studies, based on convolutional neural networks, achieved promising results in urban land use classification. Nevertheless, they also present certain drawbacks. For instance, they require a substantial number of data for network training. If the data set does not contain sufficient images, the performance may deteriorate, and the training time and computational resource consumption are significant. Moreover, these models often suffer from issues such as large model size, slow inference speed, and difficulties in deploying on mobile devices. Models that do not rely on external devices and can be deployed offline on mobile terminals are more aligned with current practical production needs. To address these challenges, a new deep learning approach, transfer learning, has emerged. Transfer learning transfers knowledge from a pre-trained model on a similar task to improve the learning of a new task, thereby significantly enhancing the network's learning efficiency and reducing training costs [16]. Transfer learning avoids the need for a large number of data for network training, significantly reducing training time, generalization error, and computational costsassociated with model construction. Transfer learning encompasses three main methods: full weight transfer, feature extraction, and fine-tuning [14]. Currently, China faces diverse challenges in the field of urban land use classification. Over the past four decades, the country has undergone rapid socio-economic development. With its large population base and swift urbanization, especially in the eastern coastal regions and certain major inland cities, high population densities and scarce land resources present significant challenges. In this context, planning for residential, industrial, commercial, and ecological land use within the confines of limited land resources is a formidable task. The refinement of urban land use classification necessitates the provision of more detailed data support. Moreover, harnessing modern technological tools such as big data and remote sensing to enhance the precision and efficiency of land use classification is among the challenges that we must currently address [40; 45]. Chinese land use type is evolving towards intensive and compact development. However, there are some problems in land use classification, such as the boundary is not easy to identify, the road is blocked by