## 1 Introduction  
Change detection in remote sensing images is a critical area of current research in geographic information science and remote sensing applications. The Earth's surface is constantly undergoing dynamic changes, including urban expansion, land use transformation, ecosystem succession, and natural disasters. Timely and accurate information on these changes is essential for understanding surface processes, monitoring environmental evolution, and making informed decisions. While traditional on-site surveys and manual interpretation can detect changes in local areas, they are limited in providing a comprehensive view of surface changes on a larger scale due to their restricted spatio-temporal scale and high workload. With the rapid advancement of remote sensing technology, high-resolution satellite images are becoming increasingly available. Remote sensing image change detection technology helps overcome these limitations, enabling systematic tracking and comprehensive assessment of surface system changes. This significantly improvesreal-time monitoring and efficiency in monitoring Earth's environment. [PERSON] et al. [1] utilized remote sensing change detection technology to evaluate urban expansion and land use changes in Egypt's Daqahlia Province over the past three decades, providing crucial information for regional planning and sustainable development. Similarly, [PERSON] et al. [2] utilized Sentinel-2 satellite data to develop a technique for generating glacier extent and surface zoning maps, allowing them to monitor spatiotemporal variations in glacier coverage. This method enhances the monitoring of glacier dynamics and provides essential data for glacier studies, hydrological modeling, and climate change assessments. The objective of change detection is to identify pixels that exhibit'semantic change' between multitemporal remote sensing images captured at different times within the same area, classifying each pixel in a region as either 'change' or 'no change' [3]. The challenge in change detection lies in ensuring that the final change map accurately captures only meaningful changes while filtering out non-semantic changes like those resulting from camera motion, sensor noise, or variations in lighting. Furthermore, defining change specifically as alterations in man-made structures such as buildings and vehicles, rather than seasonal variations, adds another layer of complexity to the task. Traditional change detection methods often rely on pixel-based or object-based approaches, which may overlook subtle changes or introduce inaccuracies due to noise or registration errors. To tackle these challenges, there is a growing interest in leveraging deep learning techniques, particularly convolutional neural networks (CNNs), for change detection tasks [4; 5; 6; 7]. Recently, 3D convolutional neural networks (3D-CNNs) have emerged as a potent tool for processing spatiotemporal data [8]. By conducting three-dimensional convolution operations on spatiotemporal data, 3D-CNNs can capture features in both spatial and temporal dimensions. Unlike 2D-CNNs and 1D-CNNs, which handle spatial or temporal features separately, 3D-CNNs excel in modeling correlations within spatiotemporal data. While traditional 2D-CNNs can only process data from a single time frame, 3D-CNNs can analyze data from multiple time frames simultaneously to grasp dynamic features along the time dimension [9]. This capability is particularly advantageous for studying change processes in sequences of remote-sensing images. The 3D-CNNs can extract features in both spatial and temporal dimensions simultaneously, capturing interdependent features in spatiotemporal data modeling. By automatically learning complex spatial-temporal interactions, 3D-CNNs provide a more discriminative representation [10]. Particularly effective in remote sensing image time series data, 3D-CNNs are an advanced approach for tasks like remote sensing change detection [11].