Land Cover Classification with CNN

À propos du projet

Designing and training a convolutional neural network (CNN) with TensorFlow/Keras on the EuroSAT satellite dataset (Sentinel-2 imagery, 10 land cover classes: forest, industrial area, highway, residential area, crops...). After 12 training epochs, the model reached 81% accuracy on the test set, performing well on visually distinct classes (forest, industrial zones, water) with more confusion between visually similar classes (highways, permanent crops).

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