SINT4CH 2025

The Spatial Intelligence for Cultural Heritage (SINT4CH) workshop explores the role of AI in preserving and interacting with historical artefacts and sites. By integrating perception, action and learning, the concept of spatial intelligence aims to advance the engagement with cultural heritage (CH). 

This workshop will explore topics such as 3D reconstruction, AI-driven restoration, and human-AI collaboration, with an emphasis on sustainable and ethical approaches to conservation. With progress in computer vision, robotic interaction, and large-scale language models, participants will explore techniques and future directions that augment human capabilities to ensure the long-term preservation and understanding of our CH. 

This workshop aims not only to serve as a venue for presenting work in this area but also to build a community and share information in this new field.

Topics

The workshop calls for submissions addressing, but not limited to, the following topics:

• Spatial computing and AI for cultural heritage documentation and preservation

  • • Photogrammetry and laser scanning for heritage documentation

    • Automated feature extraction from large-scale heritage data

    • Temporal monitoring of heritage sites using AI

    • AI for historical map vectorization

    • Spatial data fusion for integrated heritage documentation (e.g. LiDAR, photogrammetry, satellite imagery) for more comprehensive documentation of heritage sites

• 3D modelling, digital twins and virtual reconstructions of historic sites

  • • 3D laser scanning and point cloud processing for cultural heritage

    • AI-based reconstruction of incomplete or damaged heritage artefacts

    • Interactive digital twins for immersive site exploration

    • AI-enabled predictive modelling for virtual reconstructions

    • Collaborative virtual environments for heritage research and conservation

• GeoAI and GIS technologies for advanced spatial analysis in cultural heritage management

  1. • GeoAI for predictive site discovery in archaeology

     • Spatial-temporal analysis of heritage landscapes using GIS and AI

     • Modelling the impact of climate change on heritage sites using GeoAI

     • Cultural landscape analysis using AI-enabled geospatial tools

• Remote sensing for CH site monitoring and conservation

  1. • Drone and satellite images for surveying large heritage sites

     • Multispectral and thermal imaging for heritage analysis

     • AI-based image analysis for real-time site monitoring

     • LiDAR-equipped drones for 3D mapping of heritage sites

• AI for object recognition and analysis in archaeological contexts

  1. • Deep learning for automatic 3D data classification

     • Object detection in satellite imagery for archaeological discovery

     • AI-based analysis of inscriptions and iconography

     • Machine learning for chronological dating of artefacts

     • Generative AI for completion or reconstruction of fragmented archaeological objects 

• Ethical challenges and opportunities in digitizing cultural heritage

  1. • Ethics of digitisation

     • Ownership of data and intellectual property in digital heritage

     • Balancing public access with preservation concerns

     • Ethical AI in the representation of sensitive cultural material

Challenge or call for dataset

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