Abstract Manager

Water hazards pose significant risks to linear infrastructure, such as railways and roadways, necessitating advanced monitoring and mitigation strategies. Traditional methods, with their limited spatial coverage, extensive resource requirements, and latency, often fall short of enabling pre-emptive interventions. In contrast, remote sensing technologies, particularly space-based Synthetic Aperture Radar (SAR) and multispectral imagery, offer comprehensive, cost-effective, and frequent data collection. When combined with advanced Artificial Intelligence (AI) algorithms, these technologies enable the identification, monitoring, and prediction of water-related hazards, ensuring more resilient and secure infrastructure management.

This paper showcases the application of space-based remote sensing technologies integrated with proprietary AI algorithms to assess water hazards for a Class 1 rail operator and highlights its scalability for roadways and other linear infrastructure. By processing massive volumes of satellite data, AI provides detailed insights into water bodies, accurately estimates water levels, and tracks changes over time. The system effectively monitors thousands of miles of corridors with a width of up to ten miles, identifying critical hazards such as beaver dams, water level fluctuations, potential washouts, and other water-related issues that threaten infrastructure integrity.

The system’s use of SAR and multispectral imagery ensures reliable, high-resolution monitoring, capable of overcoming weather-related constraints and capturing data across various wavelengths. Proprietary AI models analyze this data to detect water hazards, with accuracy validated through ground truth inspections and performance metrics, including precision, recall, and F1-score. By integrating near real-time alerts, operators are empowered to implement preventive measures proactively, reducing risks and ensuring operational safety and efficiency.

This study underscores the transformative potential of combining AI and remote sensing technologies for water hazard monitoring across rail and road infrastructure. With climate change intensifying the frequency and severity of extreme weather events, this approach enhances infrastructure resiliency and adaptability, ensuring safer and more reliable operations for transportation networks. The methodology discussed is scalable to various linear assets, such as highways, pipelines, and utility corridors, presenting a forward-looking solution for addressing challenges in an era of environmental uncertainty and evolving infrastructure demands.