# 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
By contrast, unsupervised learning methods capitalize on unlabeled data but face challenges in extracting meaningful patterns from raw data. Even if abundant, unlabeled data still need domain-specific insight to interpret clustered or anomalous findings in a meaningful way. Noise, outliers, and dynamic and heterogeneous railway environments can hinder consistent pattern discovery, especially in SAR-gathered data. The inherent noise in SAR-acquired images [69; 70] hinders the extraction of reliable information and requiring of advanced techniques to enhance data quality [71]. Furthermore, heterogeneous environment (such as varying terrain and vegetation) introduce additional challenges, as they can cause scattering and signal distortion, preventing the detection of patterns in railway infrastructure [72]. Mitigation approaches include semi-supervised learning and self-supervised learning [73]. [PERSON] et al. [74] proposed a semi-supervised approach to address imbalanced image data in rail defect detection, improving the model's ability to identify defects with fewer labeled samples, an approach that can be leveraged to EO-based applications. While specific applications of self-supervised learning in railway infrastructure monitoring via EO-based remote sensing are still missing, in the broader field of remote sensing, self-supervised learning techniques to enhance feature extraction and change detection are emerging. Self-supervised learning has been used by [PERSON] et al. [65] to pretrain deep convolutional neural network (CNN) models on large unlabeled datasets,which are then fine-tuned with limited labeled data, demonstrating improved accuracy and generalization. [PERSON] et al. [75] proposed a self-supervised representation learning method based on temporal prediction to improve change detection in remote sensing images, which could be adapted for EO-based railway infrastructure monitoring changes. [PERSON] et al. [76] demonstrated that self-supervised learning could enhance scene classification in remote sensing, potentially benefiting novel EO-based railway infrastructure monitoring approaches like the ones proposed in [70]. Augmenting the feature extraction process with expert-driven rules or priors can also direct unsupervised learning models toward better results. [PERSON] et al. [77] proposed a method that incorporates fuzzy rules into deep learning architecture, enhancing interpretability and performance in image feature extraction and analysis. [PERSON] et al. [78] showed that extracting rules in unsupervised anomaly detection enhances the explainability and effectiveness of the AI model. The analyzed approaches could help both supervised and unsupervised learning methods to overcome the data scarcity hurdle characteristic of EO-based railway infrastructure monitoring and achieve better generalization, showing promise that AI-driven analytics in EO-based railway infrastructure monitoring can become more reliable and scalable.