# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
## 3 EO-Based Railway Infrastructure Monitoring: Literature Review
### Limitations in the Existing EO-Based Methodologies, Datasets, and Technologies  
Despite the promising capabilities of EO data for railway infrastructure monitoring, significant limitations remain in the methodologies, datasets, and technologies employed. First, the reliance on synthetic aperture radar (SAR) data, while effective for deformation monitoring, lacks the integration of optical, thermal, and multispectral data, which can provide a more comprehensive analysis. Non-commercial optical data are often of insufficient resolution, and high-resolution commercial data are prohibitively expensive for large-scale use [14; 15]. Additionally, the limited revisit times of satellites, particularly for dynamic phenomena such as flash floods or rapid deformation, hinder the ability to conduct near-real-time monitoring. For example, Sentinel-1 offers high spatial resolution but is constrained by a six-day revisit cycle [20]. Another critical limitation is the integration of EO data with in situ measurements and AI-driven analytics. While promising, the lack of robust frameworks to combine these approaches limits their scalability and predictive capabilities. Current AI models trained on EO data are not fully optimized for handling multimodal data inputs, leading to suboptimal outcomes in predictive modelling [18; 21]. Moreover, atmospheric interference, such as cloud cover, significantly impacts optical sensor performance, limiting their utility in adverse weather conditions [22]. Addressing these limitations through advanced fusion [53; 56; 61; 62] methodologies and enhanced satellite technologies is imperative for the evolution of EO-based monitoring systems. Another important aspect to address is that with new generations of sensors, the complexity of remote sensing scenes has increased significantly due to increased resolution [63]. Although numerous authors over the years coped with low resolution of images and proposed new approaches [47; 64] for mitigating the low resolution induced problems new very high resolution (VHR) images generate additional challenges for remote sensing scene classification [65]. Selecting the appropriate learning approach for AI-driven analytics in EO-based railway infrastructure monitoring is challenging due to the inherent limitations of both supervised and unsupervised learning methods. In supervised methods, the core challenge lies in acquiring sufficiently large labeled datasets, which is especially problematic with some applications of EO-based railway infrastructure monitoring like damages listed in [30], caused by weather effects and infrastructure and earthwork failures like embankment and cutting failures, flooding, objects like fallen trees on the line, or rockslides. Furthermore, the process of dataset labeling is expensive, time-consuming, and reliant on specialized expertise. With insufficient data labels for AI models, there is a high chance of overfitting on a narrow provided sample, which is especially problematic in dynamic and heterogeneous railway infrastructure environments that demand wide coverage and frequent updates. To mitigate this, data augmentation techniques like synthetic generation of labeled samples [66] and active learning, where human analysts label only the most uncertain cases, can reduce the burden of manual dataset annotation [67]. For synthetic generation of labeled samples, new approaches based on Generative AI models, such as Generative Adversarial Networks (GANs), are proving instrumental in creating synthetic labeled datasets by learning the statistical properties of supplied real data and producing new labeled examples that closely mimic the original limited dataset [68]. Additionally, transfer learning, where models pretrained on similar monitoring problems or domains are fine-tuned with relatively few labeled examples which can extend the applicability of supervised models to new application or remote sensing technologies [15; 65].