Toward Robust Semantic Segmentation in Levee Infrastructure Monitoring: Enhancing Accuracy with High-Fidelity Synthetic Data and Ensemble Learning

Abstract: Levees serve as critical flood protection structures, but failures due to inadequate maintenance and extreme water pressures have led to devastating events such as Hurricane Katrina. Manual inspections are slow, labor-intensive, and prone to human error, necessitating the development of automated solutions. This study proposes an AI-driven framework for levee inspection utilizing deep learning-based semantic segmentation to detect rutting and enhance the identification of sand boils. To address dataset limitations, high-fidelity synthetic images are generated using DreamBooth for fine-tuning, while ControlNet adds structural constraints to enhance realism and consistency. A semi-automatic convex hull annotation technique enhances labeling efficiency, and ensemble learning strategies further improve segmentation accuracy. The system integrates real-time inference capabilities within a web-based platform, enabling rapid and precise identification of defects. By combining deep learning, synthetic data augmentation, and real-time deployment, this research presents a scalable, innovative solution for automated levee monitoring, addressing key challenges in flood risk management and infrastructure resilience. Keywords: Levee Inspection, Deep Learning, Semantic Segmentation, Synthetic Data, Real-time Monitoring, Generative AI.