PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
October 2, 2025˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences0 citationsOpen Access

HERITALISE. Project Insights and Initial Developments

View Full Paper
FCFiliberto ChiabrandoALAndrea Maria LinguaASAlessandra Spreafico

Key Points

  • Cultural heritage digitisation improves understanding and public engagement, requiring tailored methods.
  • Common techniques like photogrammetry and laser scanning are integral to documenting cultural heritage.
  • The HERITALISE project focuses on holistic methods for capturing both visible and non-visible cultural heritage features.
  • The integration of AI and other technologies advances the capability of cultural heritage digitisation.

Abstract

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.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Chiabrando et al. (2025) studied this question.

synapsesocial.com/papers/68de5da283cbc991d0a20a29https://doi.org/10.5194/isprs-archives-xlviii-m-9-2025-269-2025
Ask AI
Helpful
Bookmark
Share
View Full Paper