Satellite data have the potential to significantly enhance railway operations and drive the digitization of the rail sector. In the context of railways, satellite data primarily refers to the use of Global Navigation Satellite System (GNSS) data for applications such as navigation, positioning, and signalling. However, remote sensing data from Earth Observation (EO) satellites remain comparatively underutilized in railway applications. While the use of GNSS data in railways is well documented in the literature, research on EO-based remote sensing methods remains relatively limited. This paper aims to bridge this gap as it presents a comprehensive review of the use of satellite data in railway applications, with a particular focus on the underexplored potential of EO data. It provides the first in-depth analysis of EO techniques, primarily examining the use of synthetic aperture radar (SAR) and optical satellite data for key applications for infrastructure managers and railway operators, such as assessing track stability, detecting deformations, and monitoring surrounding environmental conditions. The goal of this review is to explore the diverse range of EO-based applications in railways and to identify emerging trends, including the integration of thermal EO data and the novel use of SAR for dynamic and predictive analyses. By synthesizing existing research and addressing knowledge gaps, the presented review underscores the potential of EO data to transform railway infrastructure management. Enhanced spatial resolution, frequent revisit cycles, and advanced AI-driven analytics are highlighted as key enablers for safer, more reliable, and cost-effective solutions. This review provides a framework for leveraging EO data to drive innovation and improve railway monitoring practices.

The growing demand for safe, efficient, and resilient railway networks has driven significant interest in leveraging Earth Observation (EO) technologies for large-scale infrastructure monitoring. This paper reviews recent advancements in EO-based railway monitoring, highlighting the transformative role of Artificial Intelligence (AI) and its challenges in processing and interpreting vast, heterogeneous EO datasets. The authors discuss emerging solutions and trends in data generation, emphasising innovations that enhance the precision and reliability of EO-derived insights for railway asset management. The enhancement of data generation is illustrated through the SPATRA and IIMEO projects, which demonstrate how to improve EO data resolution and how to integrate multi-source data fusion for training AI models that enhance monitoring accuracy.