Identifying changes in Earth's phenomena is vital for understanding and mitigating the impacts of environmental issues. Monitoring Earth's surface phenomena can be done effectively using satellite images acquired at different times. In addition to spectral features, spatial features play a significant role in detecting precise changes. However, classical change detection (CD) methods rarely consider spatial information and fail to account for scale variations within images. The present introduces a novel deep learning-based CD method that hierarchically extracts spatial-spectral features in various scales to address these issues. The proposed deep neural network generates a binary change map by employing a multi-scale approach that integrates the information of patches of varied sizes at the decision level. We conducted experiments using Landsat-8 images from Sahand City, East Azarbaijan, Iran, because of their remarkable capacity to represent Earth's surface details. Tabriz's population growth has led to rapid development in Sahand city to accommodate citizens. Studying these changes can offer valuable insights into urban planning. The performance of the proposed deep model is evaluated in comparison to two classical methods, including the Change Vector Analysis (CVA) method and a random forest (RF) algorithm. Based on the change detection results, the proposed deep learning network demonstrates a significant improvement in the kappa coefficient (K.C.) compared to the RF and CVA methods, with an increase of approximately 11.86% and 29.36%, respectively. Furthermore, in terms of overall accuracy (O.A.), the proposed network outperforms both the RF and CVA methods by approximately 17.08% and 29.16%, respectively. The proposed multi-scale deep network performs better in detecting changes across all metrics. As a result, CVA fails to identify changes with sufficient accuracy.