Photogrammetric 3D Digitisation of Bajuni and Pokomo Cultural Artefacts: A 65-Artefact Proof-of-Concept at Fort Jesus, Kenya

Authors

  • Anwar Ahmed Technical University of Mombasa, School of Business, Kenya Author
  • Ashikoye Okoko National Museums of Kenya, Multimedia Unit, Kenya Author
  • Collins Kiprotich LAIPCH-K Technical Team — 3D Reconstruction Author

DOI:

https://doi.org/10.61250/ssmj/v1.i4.12

Keywords:

Artifacts, Photogrammetry, Bajuni, Pokomo, cultural, heritage, 3D, Artificial Intelligence

Abstract

Digital ‌preservation ‌opens ‌a practical route for protecting cultural objects that face material decay, shifting environmental conditions, limited accessibility, and the gradual erosion of contextual knowledge. This article documents a practice-oriented deployment of photogrammetric 3D digitisation carried out under the Leveraging Artificial Intelligence for the Preservation and Promotion of Coastal Cultural Heritage in Kenya (LAIPCH-K) project. Attention is placed on artefacts accessed via the National Museums of Kenya at Fort Jesus in Mombasa, with particular emphasis on how photographic capture, technology choices, local computing capacity, 3D reconstruction, quality checks, and metadata handling were brought into a single production workflow. The production log covers 65 cultural artefacts, and every object is supported by photographic documentation. For 44 artefacts, explicit image totals were recorded, amounting to 7,283 photographs. Per-object counts span 80 to 210 images, while the average is about 166 photographs per artefact. Work proceeded through image sorting and inspection, alignment of camera poses, Structure-from-Motion processing, dense Multi-View Stereo reconstruction, mesh creation, texture generation, post-processing cleanup, model refinement for use and dissemination, and export. To determine an appropriate toolchain, three candidate options were assessed, Meshy multi-view image-to-3D, Agisoft Metashape Professional, and 3DF Zephyr Lite. The comparison considered image constraints, licensing and cost, where processing would occur, and the limits imposed by available computing hardware. On this basis, 3DF Zephyr Lite was chosen for the locally executed photogrammetry pipeline. Overall, the study frames museum digitisation under resource constraints as a socio-technical undertaking. Successful production depended not only on capture and reconstruction steps, but also on the coordination of software, computing infrastructure, technical know-how, metadata practices, and systematic quality assurance. The resulting digital assets form an evidence-linked base for later digital-heritage work, including metadata expansion, search and retrieval, classification, multilingual access, and immersive forms of engagement. While the work establishes operational viability and reports production outputs, it does not provide an independent assessment of geometric accuracy or metrological performance.

Downloads

Download data is not yet available.

Author Biography

  • Ashikoye Okoko, National Museums of Kenya, Multimedia Unit, Kenya

     

     

References

Acke, L., Corradi, D., & Verlinden, J. (2024). Comprehensive educational framework on the application of 3D technologies for the restoration of cultural heritage objects. Journal of Cultural Heritage, 66, 613–627. https://doi.org/10.1016/j.culher.2024.01.013

Awuor, A. S., Kamau, G. W., & Owano, A. (2023). The role of digital humanities in the preservation of indigenous knowledge at the National Museums of Kenya. Regional Journal of Information and Knowledge Management, 8(2), 170–180. https://doi.org/10.70759/ngsxfa65

Carroll, S. R., Garba, I., Figueroa-Rodríguez, O. L., et al. (2020). The CARE principles for Indigenous data governance. Data Science Journal, 19, 43. https://doi.org/10.5334/dsj-2020-043

Farella, E. M., Morelli, L., Rigon, S., Grilli, E., & Remondino, F. (2022). Analysing key steps of the photogrammetric pipeline for museum artefacts 3D digitisation. Sustainability, 14(9), 5740. https://doi.org/10.3390/su14095740

Guidi, G., Gonizzi Barsanti, S., Micoli, L. L., & Russo, M. (2015). Massive 3D digitization of museum contents. In L. Toniolo, M. Boriani, & G. Guidi (Eds.), Built heritage: Monitoring conservation management (pp. 335–346). Springer. https://doi.org/10.1007/978-3-319-08533-3_28

Hess, M., Robson, S., Serpico, M., Amati, G., Pridden, I., & Nelson, T. (2016). Developing 3D imaging programmes—Workflow and quality control. Journal on Computing and Cultural Heritage, 9(1), Article 1, 1–11. https://doi.org/10.1145/2786760

Iakovaki, E., Konstantakis, M., Giaourtsakis, I., & Rentoumi, E. (2026). When reality meets practice: Challenges and pitfalls in 3D digitization using structured light scanning and photogrammetry in cultural heritage. Information, 17(3), 237. https://doi.org/10.3390/info17030237

Li, F., Achille, C., Vassena, G. P. M., & Fassi, F. (2025). The application of three dimensional digital technologies in historic gardens and related cultural heritage: A scoping review. Heritage, 8(2), 46. https://doi.org/10.3390/heritage8020046

Morita, M. M., Loaiza Carvajal, D. A., & Bilmes, G. M. (2022). New photogrammetric systems for easy low-cost 3D digitisation of cultural heritage. In Handbook of Cultural Heritage Analysis (pp. 1439–1464). Springer. https://doi.org/10.1007/978-3-030-60016-7_49

Morita, M. M., Loaiza Carvajal, D. A., González Bagur, I. L., & Bilmes, G. M. (2024). A combined approach of SfM-MVS photogrammetry and reflectance transformation imaging to enhance 3D reconstructions. Journal of Cultural Heritage, 68, 38–46. https://doi.org/10.1016/j.culher.2024.05.008

Orzechowski, M., Opioła, Ł., Lamata Martínez, I., & Ioannides, M. (2026). Integrated data, metadata, and paradata management system for 3D Digital Cultural Heritage objects: Workflow automation, federated authentication, and publication. Future Generation Computer Systems, 174, 107964. https://doi.org/10.1016/j.future.2025.107964

Pavlidis, G., Koutsoudis, A., Arnaoutoglou, F., Tsioukas, V., & Chamzas, C. (2007). Methods for 3D digitization of cultural heritage. Journal of Cultural Heritage, 8(1), 93–98. https://doi.org/10.1016/j.culher.2006.10.007

Paolanti, M., Frontoni, E., & Pierdicca, R. (2026). Towards trustworthy AI in cultural heritage. npj Heritage Science, 14, 131. https://doi.org/10.1038/s40494-026-02403-z

Rahaman, H., & Champion, E. (2019). To 3D or not 3D: Choosing a photogrammetry workflow for cultural heritage groups. Heritage, 2(3), 1835–1851. https://doi.org/10.3390/heritage2030112

Remondino, F., & Rizzi, A. (2010). Reality-based 3D documentation of natural and cultural heritage sites—Techniques, problems, and examples. Applied Geomatics, 2(3), 85–100. https://doi.org/10.1007/s12518-010-0025-x

Stoean, R., Bacanin, N., Stoean, C., & Ionescu, L. (2024). Bridging the past and present: AI-driven 3D restoration of degraded artefacts for museum digital display. Journal of Cultural Heritage, 69, 18–26. https://doi.org/10.1016/j.culher.2024.07.008

Sullini, M., et al. (2026). When meshes lie: Formalising tacit knowledge for flaw recognition in cultural heritage photogrammetry. Digital Applications in Archaeology and Cultural Heritage, 41, e00534. https://doi.org/10.1016/j.daach.2026.e00534

UNESCO. (2025). Data Governance Toolkit: Navigating Data in the Digital Age. UNESCO.

Vannini, E., Dal Fovo, A., & Fontana, R. (2026). Three-dimensional surveying with optical sensors in heritage science: A review. Sensors, 26(8), 2297. https://doi.org/10.3390/s26082297

Wang, Y., Cui, H., Du, J., et al. (2025). Single-image 3D reconstruction of painted potteries using AI diffusion and feedforward models. npj Heritage Science, 13, 545. https://doi.org/10.1038/s40494-025-02114-x

Wanyama, P. M., Chiguyaso, M. M., & Kaingu, G. G. (2023). The condition assessment study of the coastal archaeological storage building and the collections at Fort Jesus Mombasa, Kenya. Journal of the Kenya National Commission for UNESCO, 2(1).

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. https://doi.org/10.1038/sdata.2016.18

Zhang, S., Wang, W., Lu, B., et al. (2025). High-fidelity 3D Buddhist sculpture reconstruction from single images using domain-adaptive diffusion. npj Heritage Science, 13, 670. https://doi.org/10.1038/s40494-025-02257-x

Downloads

Published

2026-10-02

How to Cite

Ahmed, A., Okoko, A. ., & Kiprotich, C. . (2026). Photogrammetric 3D Digitisation of Bajuni and Pokomo Cultural Artefacts: A 65-Artefact Proof-of-Concept at Fort Jesus, Kenya. SOUTH SAHARA MULTIDISCIPLINARY JOURNAL, 4(1), 220-243. https://doi.org/10.61250/ssmj/v1.i4.12

Article Metrics

Check out the metrics for this article:

See PlumX Metrics