Satellite Images Classification using Deep Learning Algorithms and Interfacing with Django

In a variety of fields, including agriculture, urban planning, disaster relief, and environmental monitoring, satellite imaging is essential. The ability to classify satellite images effectively and accurately is essential for deriving insightful information and supporting well-informed decision-making processes. In this proposed work, we leverage artificial intelligence (AI) techniques to achieve unprecedented success in satellite picture classification.The suggested approach focuses on compiling a large dataset of annotated satellite photos that represent different categories of land cover or interesting things. This dataset is carefully preprocessed in order to improve image quality, remove noise, and normalize the data—laying a solid basis for further analysis. We use data augmentation methods like rotation, scaling, and flipping to improve the model's ability to generalize and enrich the dataset.