By Stuart Kerr, Technology Correspondent
Published: 24 July 2025
Last Updated: 24 July 2025
Contact: liveaiwire@gmail.com | Twitter: @LiveAIWire
Author Bio: About Stuart Kerr
A medieval manuscript in Istanbul. A faded fresco in Florence. A cracked temple relief in Angkor Wat. These aren’t just historical treasures — they’re data points. Across the globe, cultural heritage is being preserved not by hand, but by AI-powered lenses that capture every detail in gigapixel resolution. Welcome to the new archive: vast, pixel-perfect, and smarter than ever before.
Memory at the Speed of Light
Digitisation of heritage isn’t new. But AI is redefining how we preserve, analyse, and even resurrect what history has tried to erase. High-resolution 2D and 3D scanning now captures surfaces down to sub-millimetre levels, while machine learning models clean, restore, and even hypothesise what time has destroyed.
As shown in Digital Dig Sites, archaeologists have long used AI to reconstruct sites. But today’s advances go further: heritage imaging is being refined by AI models trained on thousands of textures, pigments, calligraphy styles, and languages.
Take Europeana’s new restoration system. Using deep learning, it fills missing elements in frescoes with suggested reconstructions, highlighting the difference between authentic and predictive in layered views. It's part science, part conservation ethics.
Behind the Infrastructure
All of this depends on infrastructure few visitors see. Invisible Infrastructure explored how AI powers everything from public transport to water supply. Cultural preservation is now part of this quiet backbone — housed in digital repositories, maintained by institutions and community-driven projects alike.
The CLIR report shows how machine learning aids in metadata extraction, language recognition, and cross-referencing between global collections. Suddenly, a Sanskrit manuscript in New Delhi can be compared with a translation in Paris — algorithmically, instantly.
Ethics in 500 Billion Pixels
But not all pixels are neutral. Who controls access to these reconstructions? What cultural permissions were granted — or assumed? The Mimeta discussion raises valid concerns about AI being used to shape narratives, erase dissent, or overstep cultural autonomy.
Meanwhile, AI in Abyss reminds us that preservation often depends on costly, exclusive tools. Will the poorest regions be excluded from digitising their own past?
These questions are also echoed in University of Miami’s case studies, where community consultation is central to how Sephardic and Caribbean archives are digitised.
The Museum in Your Pocket
Interactive exhibits once required bulky VR headsets. Now, mobile AR overlays let users view gigapixel scans of artworks from their phones. A MDPI paper highlights how digital museums are building AI-guided experiences that adapt to the viewer’s language, age, and interests.
What’s more, AI isn’t just displaying — it’s interpreting. Sentiment analysis on visitor feedback helps curators optimise layout. AI interpreters translate audio tours in real-time. Accessibility is no longer an afterthought — it’s embedded.
Toward a Shared Digital Past
The OAPEN report provides a comprehensive overview of technical approaches in heritage digitisation, from LiDAR to photogrammetry. But the question now isn’t how to capture history. It’s how we choose to remember it — and who decides what’s remembered.
As stewards of culture turn to algorithms, we must build systems that are open, traceable, and respectful of origin communities. After all, memory isn’t just data. It’s identity.
About the Author
Stuart Kerr is the Technology Correspondent for LiveAIWire. He writes about artificial intelligence, ethics, and how technology is reshaping everyday life. Read more