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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.

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Recent Advancements in EO-based Monitoring of Railway Infrastructure

  • 会议论文

  • 发表日期：

    2026年

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  • Milan Banić,
  • Danijela Ristić-Durrant,
  • Alina Klapper,
  • Dietrich Kuhn,
  • Miloš Simonović,
  • Szabolcs Fischer

摘要

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.