<p>Accurate segmentation of water bodies in satellite imagery is essential for environmental monitoring, resource management, and disaster response. This study introduces a novel deep learning framework that leverages image embeddings from a pre-trained SegFormer-B4 encoder (MiT-B4) without fine-tuning, combined with a U-Net decoder, to achieve precise water body segmentation in Sentinel-2 satellite imagery. Unlike conventional methods that rely on full fine-tuning, this approach significantly reduces computational cost and training time while maintaining high segmentation accuracy. The extracted transformer-based embeddings capture both local and global spatial features, serving as input to a lightweight U-Net decoder that efficiently reconstructs segmentation masks. Experimental results demonstrate that the proposed method outperforms the fine-tuned SegFormer-B4 model in training and testing scenarios, achieving superior Intersection over Union (IoU), F1 Score, Precision, and Recall. Despite the absence of fine-tuning, the SegFormer-B4 encoder effectively extracts meaningful spatial representations, enabling accurate segmentation with minimal computational overhead. This work highlights the advantages of integrating pre-trained transformer embeddings with a dedicated segmentation decoder, offering a scalable and efficient solution for water body mapping in remote sensing applications.</p>