HERITALISE. Project Insights and Initial Developments
Journal
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Date Issued
2025
DOI
10.5194/isprs-archives-XLVIII-M-9-2025-269-2025
Abstract
Cultural Heritage (CH) encompasses a broad spectrum of tangible and intangible assets, from artifacts and architecture to landscapes and traditions. These require diverse and complex data for documentation, study, and preservation. Technological advancements have significantly improved how CH is digitised, enhancing understanding and access. Digital records preserve historical, aesthetic, and scientific values while supporting public engagement. However, there remains no universal standard for CH digitisation, with approaches often tailored to each project based on various technical and contextual factors.
Digitisation methods depend on object-specific complexity criteria such as size, material and their condition, and location, requiring multidisciplinary collaboration. Common techniques are usually employed like laser scanning, photogrammetry and structured light, while AI and emerging technologies are expanding the capabilities of advancing digitization and visualization. In the present paper the EU HERITALISE project is presented, which addresses current limitations by developing advanced methods for capturing holistically both visible and non-visible CH features. It extends frameworks like H (Holistic)-HBIM to a Memory twin, integrating multimodal and complex data types in four (4) selected demo sites presented in this paper.
Digitisation methods depend on object-specific complexity criteria such as size, material and their condition, and location, requiring multidisciplinary collaboration. Common techniques are usually employed like laser scanning, photogrammetry and structured light, while AI and emerging technologies are expanding the capabilities of advancing digitization and visualization. In the present paper the EU HERITALISE project is presented, which addresses current limitations by developing advanced methods for capturing holistically both visible and non-visible CH features. It extends frameworks like H (Holistic)-HBIM to a Memory twin, integrating multimodal and complex data types in four (4) selected demo sites presented in this paper.
File(s)![Thumbnail Image]()
Name
HERITALISE.pdf
Size
1.89 MB
Format
Adobe PDF
Checksum (MD5)
a76fa1099a2f8a4d6f88ce5147d476da