Development of a Metadata Quality Index (MQI) for Bathymetric Data Assessment in Marine Spatial Data Infrastructure
Authors
Department of Surveying and Geodesy, Faculty of Geomatics, Sabaragamuwa University of Sri Lanka (Sri Lanka)
Department of Surveying and Geodesy, Faculty of Geomatics, Sabaragamuwa University of Sri Lanka (Sri Lanka)
Article Information
DOI: 10.51244/IJRSI.2026.13020067
Subject Category: Marine Science
Volume/Issue: 13/2 | Page No: 759-777
Publication Timeline
Submitted: 2026-02-15
Accepted: 2026-02-20
Published: 2026-03-02
Abstract
Marine Spatial Data Infrastructure (MSDI) serves as the foundational framework for managing and disseminating marine geospatial data, with bathymetric data being one of its most critical components. However, the usability and reliability of bathymetric datasets within MSDI are fundamentally dependent on the quality and completeness of accompanying metadata. This paper introduces a new Metadata Quality Index (MQI) framework is presented with special focus on bathymetric data evaluation of the MSD. MQI framework measures metadata completeness on eight fundamental categories including general information, identification information, description, extent, accuracy parameters, point of contact, meta-metadata, and processing information. A weighted grading scale (0-100) was created on the basis of the importance of individual parameter in data accuracy and a decision to be made by a user. The framework was applied to 12 bathymetric datasets (1991-2024) from Sri Lanka, covering various technologies including Single-Beam Echo Sounders (SBES), Multi-Beam Echo Sounders (MBES), and modern integrated systems. Findings showed that there were strong temporal patterns in metadata quality with pre-2000 datasets having a mean MQI of 18.4 (Poor), 2000-2010 datasets had a mean MQI of 32.7 (Fair), 2010-2020 datasets had a mean MQI of 71.3 (Good) and post-2020 datasets had a mean MQI of 78.5 (Good). The most significant gaps were found during all periods, with the most significant being the lack of any calibration documentation (0% compliance) and the systematic missing links in uncertainty reporting (8.3% compliance in pre-2010 datasets). The suggested MQI framework is a quantitative, standardized assessment of metadata quality tool that may be used to offer evidence-based prioritization of retrospective documentation tasks and set minimum metadata requirements to integrate MSDI. The study will add to the operationalization of the quality management of MSDI and the FAIR (Findable, Accessible, Interoperable, Reusable) principles of data in the marine scene.
Keywords
Metadata Quality Index, Bathymetric Data, Marine Spatial Data Infrastructure, Data Quality Assessment, FAIR Principles
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References
1. Bruce, T. R., & Hillmann, D. I. (2004). The continuum of metadata quality: Defining, expressing, exploiting. In Metadata in practice (pp. 238-256). ALA Editions. [Google Scholar] [Crossref]
2. Foglini, F., & Grande, V. (2023). A Marine Spatial Data Infrastructure to manage multidisciplinary, inhomogeneous and fragmented geodata in a FAIR perspective: The Adriatic Sea experience. Oceanologia, 65(1), 260-277. [Google Scholar] [Crossref]
3. Hare, R., Eakins, B., & Amante, C. (2011). Modelling bathymetric uncertainty. The International Hydrographic Review, (6), 31-42. [Google Scholar] [Crossref]
4. Hasara, K. M. D., Perera, A. N. D., & Gunathilaka, M. D. E. K. (2025). Managing diverse datasets to ensure bathymetric data accuracy in Marine Spatial Data Infrastructure (MSDI). Journal of Geomatics Sabaragamuwa, 5(2), 1-20. [Google Scholar] [Crossref]
5. IHO. (2019). *B-11 The IHO-IOC GEBCO Cook Book*. International Hydrographic Organization. [Google Scholar] [Crossref]
6. IHO. (2022). *B-12 Guidance to crowdsourced bathymetry* (3rd ed.). International Hydrographic Organization. [Google Scholar] [Crossref]
7. IHO. (2024). *B-13 Guidance to satellite derived bathymetry* (1st ed.). International Hydrographic Organization. [Google Scholar] [Crossref]
8. IHO. (2023). *C-17 Spatial Data Infrastructures "The Marine Dimension" Guidance for Hydrographic Offices* (3rd ed.). International Hydrographic Organization. [Google Scholar] [Crossref]
9. IHO. (2022). *S-44 IHO standards for hydrographic surveys* (6.1.0 ed.). International Hydrographic Organization. [Google Scholar] [Crossref]
10. IHO. (2020). *S-67 Mariners' guide to accuracy of depth information in Electronic Navigational Charts (ENC)* (1st ed.). International Hydrographic Organization. [Google Scholar] [Crossref]
11. IHO. (2010). *S-100 Universal hydrographic data model* (1st ed.). International Hydrographic Organization. [Google Scholar] [Crossref]
12. IHO. (2022). *S-102 Bathymetric surface product specification* (2.1.0 ed.). International Hydrographic Organization. [Google Scholar] [Crossref]
13. ISO. (2014). *ISO 19115-1:2014 Geographic information — Metadata — Part 1: Fundamentals*. International Organization for Standardization. [Google Scholar] [Crossref]
14. ISO. (2019). *ISO 19115-2:2019 Geographic information — Metadata — Part 2: Extensions for acquisition and processing*. International Organization for Standardization. [Google Scholar] [Crossref]
15. ISO. (2013). ISO 19157:2013 Geographic information — Data quality. International Organization for Standardization. [Google Scholar] [Crossref]
16. Kearns, T. A., & Breman, J. (2010). Bathymetry: The art and science of seafloor modeling for modern applications. In Ocean globe (pp. 1-36). ESRI Press. [Google Scholar] [Crossref]
17. Li, Z., Peng, Z., Zhang, Z., Chu, Y., Xu, C., Yao, S., Garcia-Fernandez, A. F., Zhu, X., Yue, Y., & Levers, A. (2023). Exploring modern bathymetry: A comprehensive review of data acquisition devices, model accuracy, and interpolation techniques for enhanced underwater mapping. Frontiers in Marine Science, 10, 1178845. [Google Scholar] [Crossref]
18. Manso-Callejo, M. A., Wachowicz, M., & Bernabé-Poveda, M. A. (2013). A metadata quality index for spatial data infrastructures. International Journal of Spatial Data Infrastructures Research, 8, 1-26. [Google Scholar] [Crossref]
19. Mayer, L. A. (2006). Frontiers in seafloor mapping and visualization. Marine Geophysical Researches, 27(1), 7-17. [Google Scholar] [Crossref]
20. Nebert, D. D. (2004). Developing spatial data infrastructures: The SDI cookbook (Version 2.0). Global Spatial Data Infrastructure. [Google Scholar] [Crossref]
21. Racetin, I., Kilić Pamuković, J., & Zrinjski, M. (2022). Role of Marine Spatial Data Infrastructure and marine cadastre in a sustainable world. Journal of Marine Science and Engineering, 10(10), 1407. [Google Scholar] [Crossref]
22. Tavra, M., Jajac, N., & Cetl, V. (2017). Marine spatial data infrastructure development framework: Croatia case study. ISPRS International Journal of Geo-Information, 6(4), 117. [Google Scholar] [Crossref]
23. Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., et al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3, 160018. [Google Scholar] [Crossref]