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This dissertation presents a deep learning approach for semantic segmentation of Amazon floodplains using multi-sensor satellite imagery, aimed at mapping and monitoring these ecologically significant areas. A novel dataset combining optical and SAR imagery was created, and the VGG16 U-Net model was found to perform best, achieving F1-Scores of 0.748-0.891. The study highlights the potential of remote sensing and deep learning techniques in improving conservation efforts and understanding changes in the Ama

Author

Anirima Ghosh

Language

EN