The conservation of stone-based cultural heritage sites is a critical concern
for preserving cultural and historical landmarks. With the advent of Large
Multimodal Models, as GPT-4omni (OpenAI), Claude 3 Opus (Anthropic) and Gemini
1.5 Pro (Google), it is becoming increasingly important to define the
operational capabilities of these models. In this work, we systematically
evaluate the abilities of the main foundational multimodal models to recognise
and classify anomalies and deterioration patterns of the stone elements that
are useful in the practice of conservation and restoration of world heritage.
After defining a taxonomy of the main stone deterioration patterns and
anomalies, we asked the foundational models to identify a curated selection of
354 highly representative images of stone-built heritage, offering them a
careful selection of labels to choose from. The result, which varies depending
on the type of pattern, allowed us to identify the strengths and weaknesses of
these models in the field of heritage conservation and restoration.

通过对主要基础多模态模型的能力进行评估，我们系统性地研究了这些模型在识别和分类有助于文化遗产保护和修复的石质元素的异常和磨损模式方面的能力，并确定了这些模型在遗产保护与修复领域的优势和劣势。