Urban change detection involves identifying and analyzing alterations in urban landscapes over time. This process is crucial for urban planning, environmental monitoring, and disaster management, as it provides insights into urban growth, land use changes, and human impact on the environment. This study focuses on Recurrent Neural Networks (RNNs) due to their ability to capture temporal dependencies, making them suitable for analyzing changes over time. In this research, an RNN model, specifically the SiamCRNN, was trained and evaluated on multiple datasets representing different urban scenarios. The model performed well in detecting urban changes, especially in datasets with higher spatial resolutions, but faced challenges with datasets characterized by high spectral range and complex urban structures. These findings underscore the importance of spatial resolution in influencing RNN model effectiveness for urban change detection tasks.