Novel Use of EO Satellite Data and AI in Railways: A Concept for Rail Buckling Risk Estimation in the SPATRA Project

This paper presents SPATRA's solution concept for estimating rail buckling risk using Earth Observation (EO) satellite data and artificial intelligence (AI). The rail buckling is a critical issue exacerbated by rising global temperatures. By integrating high-resolution land surface temperature (LST) satellite data, rail track temperature prediction models, and AI-driven lateral displacement estimation, the proposed approach would enable proactive railway infrastructure management, as it allows for early identification of high-risk rail segments, reducing the likelihood of track deformation and train derailments. Initial SPATRA findings demonstrate the feasibility of using EO-based monitoring for real-time risk assessment, minimizing maintenance costs and operational disruptions. The proposed solution improves upon traditional ground-based methods by providing large-scale, continuous monitoring. Future validation efforts will refine prediction models and enhance their adaptability to various railway networks.