Inteligencia artificial en el arte rupestre
Límites, posibilidades y desafíos de una evolución incierta
Bibliographic Data
| ID | 12845850 |
|---|---|
| Authors | Hugo De Lara López (0009-0004-0564-7222, Universidad Nacional de Educación a Distancia, corresponding author) |
| Year | 2026 |
| Volume | 25 |
| Issue | 2 |
| Pages | 33-46 |
| Publication date | 2026-01-12 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Salduie (JOURNAL) |
| Journal identifiers | ISSN: 1576-6454 • E-ISSN: 2794-0055 |
| Publisher | Universidad de Zaragoza (PUBLISHER) |
| DOI | 10.26754/ojs_salduie/sald.2025212652 |
| OpenAlex | W7123571229 |
| Language | ES |
| References cited | 22 |
The recent development of artificial intelligence (AI) has opened new possibilities for the documentation and analysis of rock art. This article critically examines the potential and the limits of using open machine learning (ML) models for the automatic detection of pictorial motifs. After reviewing the state of the art and outlining the principles of ML as applied to archaeology, we empirically evaluate an open model (Roboflow 3.0 Object Detection) on four rock art panels from the Iberian Peninsula. The results show acceptable performance only in well-preserved contexts, while accuracy decreases notably in the presence of faded pigments or irregular rock surfaces. We conclude that ML can support archaeological research but cannot replace expert interpretation, and that progress in this field depends on standardized public repositories and coherent expert annotations
Documentation · Expert system · Field (mathematics · Object (grammar · Object detection · State (computer science · Aesthetic Perception and Analysis · Archaeology and Rock Art Studies · Image Processing and 3D Reconstruction
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The Fair Guiding Principles for scientific data management and stewardship
Artificial Intelligence and Prospects for its Use in the Study of Upper Palaeolithic Rock Art
The Study of Pigments in Cultural Heritage
Artificial Intelligence in Archaeological Site Conservation
Artificial Intelligence, 3D Documentation, and Rock Art—Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images
Experiencing Ancient Buildings from a 3D GIS Perspective
Reconstructing rock art chronology with transfer learning
On the use of Machine Learning methods in rock art research with application to automatic painted rock art identification
From 3D reconstruction to virtual reality
Predicting rock art sites in the Pajeú watershed, Brazil
The data deluge
| Highly cited | No |
|---|