Habaek: High-performance water segmentation through dataset expansion and inductive bias optimization

TL;DR

Habaek enhances water segmentation accuracy by dataset expansion and inductive bias optimization, demonstrating superior performance and efficiency, suitable for real-time disaster response and water management applications.

Contribution

This work introduces the Habaek model, combining dataset augmentation and inductive bias tuning, to significantly improve water segmentation performance and efficiency.

Findings

Habaek achieves IoU of 0.91986 to 0.94397 in water segmentation.

Habaek outperforms existing models in F1-score, recall, accuracy, and precision.

Data augmentation and LoRA reduce processing complexity while maintaining accuracy.

Abstract

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.