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

TL;DR

This research proposes upgrading the SegFormer model for water segmentation by data augmentation with datasets such as ADE20K and RIWA to boost generalization and shows that the suggested Habaek model outperforms current models in segmentation, indicating its potential for real-world applications.

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.

Figures (4)

• Figure 1: Architecture of SegFormer: Hierarchical transformer design for semantic segmentation
• Figure 2: Segmentation results showing river detection on the LuFi dataset.
• Figure 3: Segmentation results highlighting small water bodies and streams on the LuFi dataset.
• Figure 4: Segmentation performance comparison on a complex terrain with water bodies in the LuFi dataset.