Land cover classification using a convolutional neural network (CNN) model trained on satellite imagery. The proposed method meticulously preprocesses the satellite imagery to rescale pixel values and augment the data with random rotations, flips, and crops, ensuring the model's robustness to variations in the data. A CNN model is then designed and trained to automatically learn the intricate spatial patterns present in the data, enabling it to distinguish between different land cover types with high accuracy. Through rigorous experimentation and model optimization, the proposed approach demonstrates superior performance compared to traditional land cover classification methods. This integration of advanced machine learning techniques with remote sensing data showcases the potential for precise and efficient land cover mapping, paving the way for advancements in environmental research, urban planning, and natural resource management.

CNN (Convolution neural network) Satellite imagery Data Augmentation Natural resource management