## 1 Introduction
Although these methods are successful in practice, a common problem is that continuous down-sampling leads to the loss of precise spatial position information, which often leads to uncertainty in the edge pixels of changing objects and a lack of determination of small objects. To this end, this study introduces a Multi-level Feature Cross-Fusion technique, combining low-order and high-order features [12; 13; 14]. Low-level features preserve detailed information for capturing small local changes, while high-level features offer semantic information for identifying higher-level change patterns. By integrating these features, the model can consider both local details and global semantics, improving the accuracy of detecting changed areas [15]. Furthermore, Multi-level Feature Cross-Fusion enhances the model's generalization ability, preventing overfitting on specific change patterns and making it more robust and suitable for diverse remote sensing scenarios. Consequently, this study utilizes the 3D-CNN method to effectively analyze data across multiple time frames, capturing both spatial and temporal two-dimensional features simultaneously. By integrating Multi-level Feature Cross-Fusion, the model preserves local low-level features while incorporating advanced global semantic information. This method enhances the pixel information of the target edge, leading to a significant improvement in the accuracy of change detection, and offering more precise data for applications such as forest environment monitoring, land resource management, disaster monitoring and emergency response, urban planning, and traffic management. The main work of this paper is as follows:1. This paper introduces a novel approach for remote sensing image change detection using 3D-CNNs and Multi-level Feature Cross-Fusion (MFCF). The method combines the temporal dynamics captured by 3D-CNNs with the merging of complementary information through Multi-level Feature Cross-Fusion to achieve accurate detection of changes in remote sensing images. 2. In convolutional neural networks, MFCF is proposed to merge high and low-level feature maps. This allows for the incorporation of both spatial and semantic information, resulting in a more comprehensive set of feature information. 3. Add a channel attention mechanism (CAM) module to the convolutional neural network. CAM is a technique that highlights the most important regions in images or time series data that contribute to the model's decision-making process. Integrating a CAM can enhance the interpretability and reliability of the model. 4. This paper presents a novel two-stage decoder that incorporates two bilinear upsampling and convolution blocks to process the feature map and then applies a Squeeze-and-Excitation (SE) attention mechanism for fine-tuning the feature map. 5. Our proposed method was validated on the LEVIR construction dataset (LEVIR-CD). The experimental results demonstrate that our network exhibits superior performance, showcasing higher accuracy and robustness. The remainder of this paper is organized as follows: Section 2 reviews existing research on remote sensing image change detection. Section 3 elaborates on the proposed methodology, covering the design of the 3D-CNNs and the multi-level feature fusion strategy. Section 4 showcases experimental results and performance evaluation using benchmark datasets. Lastly, Section 5 wraps up this paper with conclusions and explores potential future research avenues.