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This study investigates machine learning techniques for cloud removal and gap filling in Sentinel-2 satellite images, focusing on the far north of Cameroon, particularly Maroua. Various algorithms, including VGG16 with data augmentation, were evaluated, with VGG16 achieving the highest accuracy of approximately 95%. The findings aim to enhance the utility of satellite imagery for land cover mapping and environmental monitoring in cloud-prone regions.

Author
nsahnyuymary
Language
EN