## 5 Conclusions  
This study presented a comprehensive nationwide adjustment of South Korea's unified geodetic control network, encompassing approximately 5560 multi-purpose control points(MPCs) established between 2008 and 2021. A unified adjustment methodology was developed by fixing 17 rigorously validated permanent GNSS stations selected for their long-term observational continuity, antenna stability, and uniform geographic distribution. The adjustment was conducted using annual GNSS observation data within the ITRF2014 reference frame and processed through the GAMIT/GLOBK software suite [44; 46; 47; 53]. Compared to legacy region-based adjustment strategies, the integrated approach significantly improved positional precision and spatial uniformity. The mean coordinate residuals were 5.1 \(\pm\) 0.057 mm in the northing direction and 5.1 \(\pm\) 0.056 mm in the easting direction, fulfilling international geodetic accuracy standards [12]. These results validate the feasibility and effectiveness of a centralized adjustment framework for producing high-precision national coordinates. Satellite geometry, multipath interference, ionospheric delay, and cycle-slip statistics were analyzed. Approximately 25.2% of the datasets failed to meet minimum quality thresholds, underscoring the need for enhanced field acquisition protocols and mandatory quality assurance procedures during national geodetic campaigns. Although the unified adjustment strategy yielded consistent and reliable results, the study highlights the critical importance of using well-validated and stably maintained control stations throughout the national network. The consistent application of such stations is essential for minimizing regional discrepancies and ensuring positional coherence on a nationwide scale. This concern is supported by recent studies that revealed significant horizontal displacements in long-established control points when proper station validation was lacking, underscoring the practical consequences of insufficient metadata and maintenance [54]. This finding also aligns with the principles established in the development of global reference frames, such as ITRF2000, where the careful selection and long-term stability of reference sites were fundamental to maintaining geodetic integrity across spatial and temporal domains [52]. It further underscores the need to implement a unified framework for station selection and metadata management to support accurate and homogeneous network adjustment. Looking forward, future research should aim to refine baseline error models, integrate atmospheric and geophysical corrections, and implement fully automated GNSS data quality control systems. Recent developments in artificial intelligence (AI) and machine learning (ML) offer promising opportunities to further enhance adjustment strategies. For instance, [PERSON] et al. (2021) [55] proposed a deep neural network model to learn complex GNSS error patterns, while [PERSON] and [PERSON] (2022) [56] applied graph convolutional neural networks to predict position corrections. [PERSON] et al. (2024) [57] introduced a reinforcement learning framework for adaptive GNSS adjustment, and [PERSON] et al. (2024) [58] demonstrated improvements in positioning accuracy through graphical neural network-based constellation modeling. In conclusion, this study proposes a transferable and scalable framework for nationwide high-precision geodetic network adjustment. It enhances the positional accuracy, consistency, and long-term reliability of Korea's spatial reference infrastructure, while also laying the foundation for future modernization through the convergence of established geodetic techniques and advanced data science methodologies. Conceptualization, S.-J.L. and H.-S.Y. ; methodology, S.-J.L. ; software, S.-J.L. ; validation, S.-J.L. and H.-S.Y. ; formal analysis, S.-J.L. ; investigation, S.-J.L. ; resources, S.-J.L. ; data curation, S.-J.L. ; writing--original draft preparation, S.-J.L. ; writing--review and editing, H.-S.Y. ; visualization, S.-J.L.