Identification of Stone Deterioration Patterns with Large Multimodal Models
This work addresses the problem of automating stone conservation for heritage professionals, but it is incremental as it applies existing models to a new dataset without novel methodological contributions.
The study evaluated large multimodal models like GPT-4omni, Claude 3 Opus, and Gemini 1.5 Pro for identifying and classifying stone deterioration patterns in cultural heritage sites using 354 curated images, revealing their strengths and weaknesses in this domain.
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.