The rapid acquisition of satellite imagery has brought tremendous challenges in processing and interpreting vast geospatial datasets. Traditional manual interpretation methods are insufficient to handle the vast volumes and complexities of satellite images generated by global Earth observation systems. These images capture intricate details across multiple spectral bands, reflecting diverse land uses, urban infrastructure, agricultural patterns, and environmental changes. Applications include disaster response, law enforcement, environmental monitoring, and urban planning, which all require accurate and efficient classification of objects and facilities over large geographic areas. Advanced, automated solutions that can handle high-resolution, multi-spectral satellite imagery are necessary to meet these demands. This paper discusses the application of deep learning techniques to address these challenges, leveraging state-of-the-art models such as MobileNet, EfficientNet, and Vision Transformers (ViTs). MobileNet and EfficientNet are lightweight and efficient architectures for large-scale image classification, while ViTs use self-attention mechanisms to capture long-range dependencies and relationships within image data. These models are combined with satellite metadata to exploit contextual information, enhancing classification accuracy and scalability. The results of the present research shows the vision transformer (ViT) is able to classify the satellite images up to the accuracy of 98%. In addition, this research incorporates various data augmentation strategies, such as random cropping, flipping, and rotation, to improve model generalization and robustness against diverse environmental conditions. This work is particularly focused on the implications that such advancements hold in real-world application, from workflow optimization of disaster management, improving environmental monitoring, to enabling sustainable urban development.