Enhancing Water Bodies Detection in the Highland and Coastal Zones Through Multisensor Spectral Data Fusion and Deep Learning

Accurate mapping of inland and coastal water bodies is crucial for monitoring environmental changes, managing hydrological resources, and assessing the impacts of climatic variability. This study presents a deep-learning-based semantic segmentation framework that leverages multiband Sentinel-2 imagery for delineating glaciers and coastal lakes. The dataset comprises 400 × 400-pixel image patches, constructed using false-color composites of Sentinel-2 bands 8 (NIR), 4 (red), and 3 (green), which enhance the spectral separation between water and nonwater surfaces. These bands were strategically selected to improve water body contrast and boundary definition through multisensor data fusion, enabling more precise lake border extraction. Each image patch is paired with hand-labeled binary lake masks to serve as ground truth. We developed and trained a simple U-Net in PyTorch and a shallow convolutional neural network in TensorFlow to evaluate model performance and architectural efficiency using the same dataset. Both models were assessed using standard performance metrics, including precision, recall, F1-score, and intersection over union (IoU). Results show high segmentation accuracy across both platforms (F1 > 0.92 and IoU > 0.86). The TensorFlow-based model exhibited faster training and inference, while the PyTorch U-Net provided more consistent and accurate border delineation. This work demonstrates the synergistic power of multiband spectral fusion and deep learning for environmental feature extraction in remote sensing. The proposed models and methods are scalable and adaptable for broader applications in coastal monitoring, inland water mapping, and climate-related hydrological assessments, offering a valuable contribution to automated Earth observation workflows under changing climatic conditions.