Identification of Stone Deterioration Patterns with Large Multimodal Models

TL;DR

This study evaluates large multimodal models' ability to recognize and classify stone deterioration patterns in heritage conservation, highlighting their strengths and weaknesses in practical restoration tasks.

Contribution

It systematically assesses foundational multimodal models' capabilities in identifying stone deterioration patterns, providing a benchmark for heritage conservation applications.

Findings

Models show varying accuracy depending on pattern type.

Strengths identified in recognizing certain deterioration features.

Weaknesses found in classifying complex or subtle anomalies.

Abstract

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.