## 4 Challenges and Limitations
### Data Quality and Availability  
The effectiveness of AI applications in maritime operations is fundamentally dependent on the availability and quality of data. High-quality data are essential for training machine learning models and ensuring their ability to make accurate predictions and informed decisions. However, obtaining such data in the maritime context is fraught with challenges due to the unique and often harsh conditions of this environment [85; 86].  
Maritime environments are dynamic and unpredictable, which complicates consistent data collection. Vessels operate in a variety of conditions, ranging from calm seas to severe storms, and across different geographic regions with varying environmental factors. This variability can lead to inconsistencies in data collection, as sensors may not function optimally under all conditions. Sensor malfunctions are a common issue, often caused by exposure to saltwater, extreme temperatures, and mechanical vibrations. These malfunctions can result in gaps or inaccuracies in the collected data [87; 88].  
Moreover, data transmission in maritime operations is often hindered by connectivity issues. Vessels frequently travel through remote areas with limited or no access to reliable communication networks, making real-time data transmission challenging. This can lead to delays in data availability and reduce the timeliness of the information used for AI-driven decision-making [89].  
In addition to these physical and technical challenges, there is also the problem of data heterogeneity. Maritime data are collected from a multitude of sources, including sensors, logs, and external databases, each potentially using different formats and standards. This inconsistency can complicate data integration and analysis, necessitating extensive preprocessing to harmonize the data into a usable format for AI applications [90; 91].  
Historical data, which is often essential for training AI models, may be incomplete or biased. Incomplete datasets can arise from sporadic data collection practices or historical limitations in technology. Bias in data can result from human errors in manual data entry or from systemic issues within the data collection processes themselves. Such biases can skew the AI models, leading to less reliable predictions and decisions [92].  
Addressing these data quality and availability issues is critical for the reliable deployment of AI in the maritime industry. Efforts must be made to standardize data collection methods across the industry to ensure consistency and comparability of data. Improving sensor technology is also crucial, focusing on enhancing durability and reliability under maritime conditions. Robust data management practices, including rigorous data validation and cleansing processes, are necessary to maintain the integrity and accuracy of the data used for AI applications [93].  
Furthermore, investing in advanced data transmission technologies, such as satellite communication systems, can mitigate connectivity issues and enhance the real-time availability of data. Collaborative initiatives among industry stakeholders to create centralized data repositories and share best practices can also help improve the overall quality and accessibility of maritime data [19; 94].  
By addressing these challenges, the maritime industry can leverage high-quality, reliable data to fully exploit the potential of AI, leading to enhanced operational safety, efficiency, and sustainability (Table 4).