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.

As global urbanization reaches an all-time high, effective urban management becomes a crucial factor for efficient development. Enhanced monitoring of these transformations leads to more informed decision-making by policymakers, emphasizing the importance of tracking these changes. One method for monitoring is Change Detection (CD), which involves comparing two satellite images captured at different times to detect changes over a period of time. CD involves numerous difficulties, such as data collection, varying weather conditions, limited availability of datasets, noise, illumination differences, and discrepancies in the equipment used for image capture. Convolutional Neural Networks (CNNs) can address these issues by delivering more effective models with better performance than non-deep learning models. However, the rise of Transformers has led researchers to develop networks based on Transformer architecture, yielding more promising results than CNNs when more data is available. This paper conducts an analysis of two existing Transformer-based models, emphasizing the challenges of handling CD with artificially small datasets. Using smaller datasets reduces the requirements for remote sensing capabilities of satellites, simulating the limitations encountered during data collection and processing. The models under examination are the Bitemporal Image Transformer (BIT) and the Visual change Transformer (VcT).