Abstract: Satellite image classification plays a crucial role in various fields such as agriculture, urban planning, disaster management, and environmental monitoring. This paper presents a novel approach utilizing TensorFlow, a popular open-source machine learning framework, for satellite image classification. The proposed methodology leverages deep learning techniques to extract meaningful features from satellite images, enabling accurate classification into predefined categories. By harnessing the power of convolutional neural networks (CNNs) implemented in TensorFlow, this research aims to enhance the efficiency and accuracy of satellite image classification tasks. The experimental results demonstrate the effectiveness of the proposed approach in achieving high classification accuracy of about 96.5% while maintaining computational efficiency. It classifies about 10 different classes obtained from the EuroSat dataset. It is implemented in Google Collab for faster training and implementation.