This paper presents a comparative analysis of two quantitative models for evaluating the reuse of cultural heritage, using fortified sites in a monofunctional city dedicated to cultural tourism, such as Venice, as a case study. The models explore three distinct reuse scenarios, assessing the appropriateness of each through a combination of fuzzy expert systems (FESs) and self-organising maps (SOMs). An FES acts as an expert-driven approach that formalises problem-solving based on external knowledge, while SOMs provide a data-driven perspective, autonomously processing and aggregating data without relying on external input or predefined assumptions. This innovative methodology facilitates the identification of new functional uses for cultural heritage by leveraging data sources related to the intrinsic structural characteristics of the assets, their territorial context and insights from external experts, alongside pre-established reuse scenarios that guide the analysis. In territories where public policies are fragmented and lack integration, this research provides a critical contribution by addressing the unbalanced distribution of functions across territories. The insights generated from this study offer practical guidance for stakeholders involved in managing cultural heritage, supporting enhanced institutional frameworks that can significantly boost the local economic complexity. This analysis showcases the potential of combining FESs and SOMs as a methodological advancement in the field of cultural heritage research. By illustrating how these tools can be applied together to address broader research challenges, the study contributes to the development of new procedures that can be adapted for use in similar contexts.

Cultural heritage reuse applying fuzzy expert knowledge and machine learning: Venice’s fortresses case study / Camatti, Nicola; di Tollo, Giacomo; Gastaldi, Francesco; Camerin, Federico. - In: REGIONAL STUDIES, REGIONAL SCIENCE. - ISSN 2168-1376. - 12:1(2025), pp. 225-251. [10.1080/21681376.2025.2472058]

Cultural heritage reuse applying fuzzy expert knowledge and machine learning: Venice’s fortresses case study

di Tollo, Giacomo;
2025-01-01

Abstract

This paper presents a comparative analysis of two quantitative models for evaluating the reuse of cultural heritage, using fortified sites in a monofunctional city dedicated to cultural tourism, such as Venice, as a case study. The models explore three distinct reuse scenarios, assessing the appropriateness of each through a combination of fuzzy expert systems (FESs) and self-organising maps (SOMs). An FES acts as an expert-driven approach that formalises problem-solving based on external knowledge, while SOMs provide a data-driven perspective, autonomously processing and aggregating data without relying on external input or predefined assumptions. This innovative methodology facilitates the identification of new functional uses for cultural heritage by leveraging data sources related to the intrinsic structural characteristics of the assets, their territorial context and insights from external experts, alongside pre-established reuse scenarios that guide the analysis. In territories where public policies are fragmented and lack integration, this research provides a critical contribution by addressing the unbalanced distribution of functions across territories. The insights generated from this study offer practical guidance for stakeholders involved in managing cultural heritage, supporting enhanced institutional frameworks that can significantly boost the local economic complexity. This analysis showcases the potential of combining FESs and SOMs as a methodological advancement in the field of cultural heritage research. By illustrating how these tools can be applied together to address broader research challenges, the study contributes to the development of new procedures that can be adapted for use in similar contexts.
2025
cultural heritage reuse; FES; fortresses; Fuzzy logic; machine learning; SOM artificial neural network; urban governance; Venice
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/344413
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