## 1 Introduction  
The current development of urbanization has led to the emergence of large-scale and complex land use clusters in cities [1; 2; 3; 4], and the information in urban remote sensing images has become more diverse [5; 6]. Classification and detection of multiple urban land types by remote sensing image interpretation is the current research trend, which has been gradually applied in the fields of land use [7; 8; 9], urban planning [10; 11], geographic information [12; 13], and other fields. Therefore, the more accurate and efficient remote sensing image interpretation techniques are urgently needed for the intelligence and automation of land use classification methods [11]. There is a lack of data sets and deep learning model applications for land use classification using civil three-channel remote sensing images in existing studies, and solving this problem will improve the practicality of land use classification techniques for remote sensing images. Traditional urban remote sensing image recognition methods have limitations such as limited extraction of information, long processing times, and low efficiency [14]. Therefore, how to recognize the information in urban remote sensing images with high efficiency and high accuracy has become a challenge for current research [15; 16; 17; 18; 19]. With research breakthroughs in computer vision methods, machine learning has transitioned from traditional shallow learning to deep learning [20; 21; 22; 23]. Deep learning, an emerging machine learning technology, models the functioning of the human brain using multi-layer neural networks and can automatically learn feature representation [24; 25; 26]. The utilization of convolutional neural networks has demonstrated inherent advantages in autonomously extracting target features, thereby enabling the application of more sophisticated algorithms for the automatic extraction of higher-level feature representations [27; 28]. The evolution of convolutional neural network models have evolved from the original five-layer structure to later developments such as Alex Net [29], VGG Net [30], Res Net [21], and Efficient Net [31; 32], and the use of convolutional neural network models for image classification has become a mainstream trend [33; 34; 35; 36]. AlexNet emerged victorious in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012, introducing deep CNNs into the computer vision community. Its application in remote sensing image classification has also demonstrated promising results [29; 37]. VGGNet is renowned for its simplicity and the exclusive use of 3 \(\times\) 3 convolutional filters. This method has been successfully applied to various remote sensing image classification tasks [30]. GoogLeNet, also known as Inception, introduced the concept of the Inception module, which enables more efficient computation and improved feature extraction. This architecture has been utilized for land use classification in remote sensing imagery, achieving high accuracy. ResNet (Residual Networks), with its introduction of skip connections and residual blocks, allows the training of very deep networks. ResNet has achieved state-of-the-art performance in many remote sensing image classification tasks [21; 38]. Despite the significant potential demonstrated by convolutional neural networks in land use classification, challenges remain, including limited labeled data, class imbalance, and model interpretability. Compared with traditional image recognition methods, deep learning networks are more effective in extracting high-level features from image information for detection, leading to constantly improving accuracy in remote sensing image classification [13; 39; 40; 41; 42]. For example, [PERSON] and [PERSON] [40] used a convolutional neural network model to classify land use in remote sensing images. In the study, they used a neural network with a deep architecture to extract image features and obtain better classification results. [PERSON] et al.