Semantic segmentation is a major challenge in remote sensing image processing. These traditional techniques often fail to achieve high accuracy due to complex surface features and multi-scale variations. This paper envisions a prediction enhancement model based on deep learning to improve segmentation accuracy and efficiency. The framework of the U-Net CNN is constructed by pre-training on a large-scale dataset. The training samples are expanded through data augmentation to ensure that the model has a certain degree of robustness. Multi-scale feature fusion is used in the training stage to pool features within each level to enhance the model’s recognition of objects of different scales. The attention mechanism is optimized together with the feature extraction process to make the model pay more attention to the important parts of the image. The overall IoU of the system on the standard dataset reaches 85%, and its deep learning enhancement system greatly improves the prediction accuracy, providing a powerful technical mnemonic aid for GIS and environmental monitoring.