This research investigates the use of convolutional Neural Networks (CNNs) for examining aerial and satellite images utilizing Tensor Flow. As high-resolution remote sensing data becomes increasingly accessible, there is an increasing demand for precise and automated analysis techniques. We employ CNNs to detect and classify complex patterns in these images, especially for land cover classification tasks. The system has been trained and validated using an extensive dataset of labeled aerial and satellite images, guaranteeing its dependability and precision across different situations. By leveraging Tensor Flow, we take advantage of its robust computational capabilities and scalability, which facilitate the development and deployment of sophisticated neural network models. The results show significant improvements in both accuracy and processing speed compared to traditional image analysis techniques. This approach has important implications for areas such as urban planning, disaster management, and environmental conservation, highlighting the transformative role of deep learning in remote sensing. Dense Net, known for connecting each layer to every other layer in a feed-forward manner, outperforms the others with an impressive 87% accuracy and a low loss of 30%, reflecting its strong learning capacity and efficient feature reuse.