Inteligencia artificial en el arte rupestre: límites, posibilidades y desafíos de una evolución incierta
DOI :
https://doi.org/10.26754/ojs_salduie/sald.2025212652Mots-clés :
arte prehistórico, machine learning, detección automática, modelos supervisados, procesamiento de imágenesRésumé
El reciente desarrollo de la inteligencia artificial (IA) ha planteado nuevas posibilidades para la documentación y el análisis del arte rupestre. Este artículo examina críticamente el potencial y los límites del uso de modelos abiertos de machine learning (ML) para la detección automática de motivos pictóricos. Tras revisar el estado de la cuestión y explicar los principios del ML aplicados a la arqueología, se evalúa empíricamente un modelo abierto (Roboflow 3.0 Object Detection) en cuatro paneles rupestres de la península ibérica. Los resultados muestran un rendimiento aceptable solo en contextos bien conservados, mientras que disminuye notablemente ante pigmentos desvaídos o soportes irregulares. Se concluye que el ML puede apoyar la investigación, pero no sustituir la interpretación arqueológica, y que su avance depende de repositorios públicos estandarizados y anotaciones expertas coherentes.
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