Collective intelligence in cultural heritage protection

Abstract

Cultural heritage protection demands targeted restoration actions in order to increase monuments' lifetime. Such actions require the use of conservation materials (e.g., consolidation materials), which can increase the durability of historic materials. However, the performance of each material on the restoration phase significantly differs with respect to its type, chemical properties and the building substrate. In this paper, we propose a new decision support architecture able to face these obstacles. The system automatically recommends to the experts the most suitable consolidation material product, among the available ones in the market. Integrated protocols are exploited, computer vision tools and artificial intelligence systems via user's feedback. The proposed architecture is evaluated using a semi-supervised learning methodology on the design of consolidation materials. © 2012 Springer-Verlag Berlin Heidelberg.