<p>The study of urban satellite imagery has undergone a significant transformation due to the emergence of deep learning techniques. This survey thoroughly examines current progress in deep learning models designed for three primary tasks: classification, segmentation, and change detection in urban environments. A total of 114 reviewed studies were analyzed, including convolutional neural networks (CNNs), hybrid models, and transformer-based models such as ResNet, UNet++, HRNet, BR-Net, Mask2Former, TransCFCCNN, and STCD-EffV2T UNet. Prominent datasets such as EuroSAT, UC-Merced, SpaceNet, DeepGlobe, LEVIR-CD, and xBD were examined alongside performance metrics like accuracy, F1 score, IoU, precision, and recall, with results visualized through comparative bar plots. Our comparison analysis indicates that models such as GoogleNet, EfficientNet-B7, and MaxViT achieve classification accuracies over 99%, while segmentation models like Mask2Former and BR-Net exhibit IoU scores over 92%. STCD-EffV2T UNet achieves exceptional F1 scores of up to 98.79% for change detection. These models are crucial for urban development and environmental change monitoring, facilitating the classification of land cover types, segmentation of buildings, roads, and urban features, as well as the detection of urban and environmental changes. This study defines growing trends and challenges, incorporating model generalization, dataset diversity, and computing complexity. It additionally suggests future directions, including the use of Mamba Networks for scalable temporal modeling, the incorporation of Vision Transformers for enhanced feature extraction, and the utilization of GANs for dataset augmentation. This paper offers a comprehensive and systematic synthesis of deep learning methodologies, establishing a fundamental reference for subsequent research in urban satellite image analysis.</p>