Railsat

RAILSAT (Railway Alert and Infrastructure Logging using Sat) is a project focused on enhancing railway infrastructure monitoring by integrating satellite imagery, AI-powered analytics, and IoT data. The objective is to develop a predictive maintenance system capable of detecting potential threats such as ground motion instabilities, vegetation encroachment, and third-party interferences along railway tracks. By processing data locally at the edge and transmitting only critical insights to the cloud, RAILSAT ensures real-time monitoring and rapid response capabilities for railway operators.

The project leverages Earth observation data to assess environmental risks affecting railways, while AI-driven predictive analytics correlates satellite images with IoT sensor data to anticipate maintenance needs. This proactive approach minimizes costly disruptions and enhances the safety and efficiency of railway operations. By integrating edge computing with cloud-based processing, RAILSAT optimizes data flow and ensures scalability, making the system adaptable for both national rail networks and regional lines.

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Latitudo 40 plays a key role in RAILSAT by providing AI-powered geospatial analytics that extract critical insights from satellite data. Through the ICOS platform, Latitudo 40 facilitates the integration of AI models, enabling the identification of infrastructure vulnerabilities in near real-time. The system continuously analyzes environmental conditions, providing early warnings that support predictive maintenance strategies. Additionally, Latitudo 40’s expertise in edge-to-cloud orchestration ensures that real-time alerts are efficiently processed and distributed to railway operators, allowing them to take preemptive action against potential threats.

By advancing the state of railway monitoring with AI and satellite intelligence, RAILSAT aims to enhance operational resilience, reduce maintenance costs, and contribute to the long-term sustainability of railway networks across Europe.

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