📝 SummarXiv

Abstract

Cultural heritage preservation faces significant challenges in managing diverse, multi-source, and multi-scale data for effective monitoring and conservation. This paper documents a comprehensive data historicity and migration framework implemented within the ARGUS project, which addresses the complexities of processing heterogeneous cultural heritage data. We describe a systematic data processing pipeline encompassing standardization, enrichment, integration, visualization, ingestion, and publication strategies. The framework transforms raw, disparate datasets into standardized formats compliant with FAIR principles. It enhances sparse datasets through established imputation techniques, ensures interoperability through database integration, and improves querying capabilities through LLM-powered natural language processing. This approach has been applied across five European pilot sites with varying preservation challenges, demonstrating its adaptability to diverse cultural heritage contexts. The implementation results show improved data accessibility, enhanced analytical capabilities, and more effective decision-making for conservation efforts.