Water segmentation is critical to disaster response and water resource management. Authorities may employ high-resolution photography to monitor rivers, lakes, and reservoirs, allowing for more proactive management in agriculture, industry, and conservation. Deep learning has improved flood monitoring by allowing models like CNNs, U-Nets, and transformers to handle large volumes of satellite and aerial data. However, these models usually have significant processing requirements, limiting their usage in real-time applications. This research proposes upgrading the SegFormer model for water segmentation by data augmentation with datasets such as ADE20K and RIWA to boost generalization. We examine how inductive bias affects attention-based models and discover that SegFormer performs better on bigger datasets. To further demonstrate the function of data augmentation, Low-Rank Adaptation (LoRA) is used to lower processing complexity while preserving accuracy. We show that the suggested Habaek model outperforms current models in segmentation, with an Intersection over Union (IoU) ranging from 0.91986 to 0.94397. In terms of F1-score, recall, accuracy, and precision, Habaek performs better than rival models, indicating its potential for real-world applications. This study highlights the need to enhance structures and include datasets for effective water segmentation.

翻译：水域分割对于灾害响应和水资源管理至关重要。相关部门可采用高分辨率摄影技术监测河流、湖泊与水库，从而在农业、工业及生态保护领域实现更主动的管理。深度学习通过使CNN、U-Net及Transformer等模型能够处理海量卫星与航空数据，显著提升了洪水监测能力。然而，这些模型通常具有较高的计算需求，限制了其在实时场景中的应用。本研究提出通过集成ADE20K与RIWA等数据集进行数据增强，以提升SegFormer模型在水域分割任务中的泛化性能。我们系统分析了归纳偏置对注意力机制模型的影响，发现SegFormer在更大规模数据集上表现更优。为进一步验证数据增强的作用，本研究采用低秩自适应（LoRA）技术在保持精度的同时降低计算复杂度。实验表明，所提出的Habaek模型在分割性能上优于现有模型，其交并比（IoU）达到0.91986至0.94397。在F1分数、召回率、准确率与精确度指标上，Habaek均超越对比模型，展现出实际应用潜力。本研究强调了优化模型结构与融合多源数据集对实现高效水域分割的重要性。