Automated Edge Detection for Cultural Heritage Conservation: Comparative Evaluation of Classical and Deep Learning Methods on Artworks Affected by Natural Disaster Damage

Automated Edge Detection for Cultural Heritage Conservation: Comparative Evaluation of Classical and Deep Learning Methods on Artworks Affected by Natural Disaster Damage

Assessing the condition of artworks is a critical step in cultural heritage conservation that traditionally involves manual damage mapping, which is time-consuming and reliant on expert input. This study, conducted within the ChemiNova project, explores the automation of edge detection using both cl...

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Bibliographic Details
Main Authors: Laya Targa, Carmen Cano, Álvaro Solbes-García, Sergio Casas, Ester Alba, Cristina Portalés
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
Subjects:
cultural heritage conservation
edge detection
image processing
damage mapping
Online Access:https://www.mdpi.com/2076-3417/15/15/8260
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